Method, apparatus, and system for people counting and recognition based on rhythmic motion monitoring

ABSTRACT

Methods, apparatus and systems for counting and recognizing objects or people based on rhythmic motion monitoring are described. A described method comprises: obtaining N1 time series of channel information (TSCI) of a wireless multipath channel that is impacted by a rhythmic motion of an object in a venue, wherein the N1 TSCI is extracted from a wireless signal transmitted from a transmitter to a receiver through the wireless multipath channel; decomposing each of the N1 TSCI into N2 time series of channel information components (TSCIC), wherein a channel information component (CIC) of each of the N2 TSCIC at a time comprises a respective component of a channel information (CI) of the TSCI at the time, wherein N1 and N2 are positive integers; monitoring the rhythmic motion of the object based on at least one of: the N1*N2 TSCIC and the N1 TSCI.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application withattorney docket number PIP506049, entitled “METHOD, APPARATUS, ANDSYSTEM FOR VITAL SIGNS MONITORING USING HIGH FREQUENCY WIRELESSSIGNALS,” filed on May 10, 2020, which is expressly incorporated byreference herein in its entirety.

The present application hereby incorporates by reference the entirety ofthe disclosures of, and claims priority to, each of the following cases:

-   -   (a) U.S. patent application Ser. No. 15/326,112, entitled        “WIRELESS POSITIONING SYSTEMS”, filed on Jan. 13, 2017,        -   (1) which is a national stage entry of PCT patent            application PCT/US2015/041037, entitled “WIRELESS            POSITIONING SYSTEMS”, filed on Jul. 17, 2015, published as            WO 2016/011433A2 on Jan. 21, 2016,    -   (b) U.S. patent application Ser. No. 16/127,151, entitled        “METHODS, APPARATUS, SERVERS, AND SYSTEMS FOR VITAL SIGNS        DETECTION AND MONITORING”, filed on Sep. 10, 2018,        -   (1) which is a continuation-in-part of PCT patent            application PCT/US2017/021963, entitled “METHODS, APPARATUS,            SERVERS, AND SYSTEMS FOR VITAL SIGNS DETECTION AND            MONITORING”, filed on Mar. 10, 2017, published as            WO2017/156492A1 on Sep. 14, 2017,    -   (c) U.S. patent application Ser. No. 16/125,748, entitled        “METHODS, DEVICES, SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS        INTERNET OF THINGS APPLICATIONS”, filed on Sep. 9, 2018,        -   (1) which is a continuation-in-part of PCT patent            application PCT/US2017/015909, entitled “METHODS, DEVICES,            SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS INTERNET OF            THINGS APPLICATIONS”, filed on Jan. 31, 2017, published as            WO2017/155634A1 on Sep. 14, 2017,    -   (d) U.S. patent application Ser. No. 15/861,422, entitled        “METHOD, APPARATUS, SERVER, AND SYSTEMS OF TIME-REVERSAL        TECHNOLOGY”, filed on Jan. 3, 2018,    -   (e) U.S. patent application Ser. No. 16/200,608, entitled        “METHOD, APPARATUS, SERVER AND SYSTEM FOR VITAL SIGN DETECTION        AND MONITORING”, filed on Nov. 26, 2018,    -   (f) U.S. Provisional Patent application 62/846,686, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS INERTIAL        MEASUREMENT”, filed on May 12, 2019,    -   (g) U.S. Provisional Patent application 62/846,688, entitled        “Method, Apparatus, and System for Processing and Presenting        Life Log based on a Wireless Signal”, filed on May 12, 2019,    -   (h) U.S. Provisional Patent application 62/849,853, entitled        “Method, Apparatus, and System for Wireless Artificial        Intelligent in Smart Car”, filed on May 18, 2019,    -   (i) U.S. patent application Ser. No. 16/446,589, entitled        “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND SENSING        USING BROADCASTING”, filed on Jun. 19, 2019,    -   (j) U.S. Provisional Patent application 62/868,782, entitled        “METHOD, APPARATUS, AND SYSTEM FOR VITAL SIGNS MONITORING USING        HIGH FREQUENCY WIRELESS SIGNALS”, filed on Jun. 28, 2019,    -   (k) U.S. Provisional Patent application 62/873,781, entitled        “METHOD, APPARATUS, AND SYSTEM FOR IMPROVING TOPOLOGY OF        WIRELESS SENSING SYSTEMS”, filed on Jul. 12, 2019,    -   (l) U.S. Provisional Patent application 62/900,565, entitled        “QUALIFIED WIRELESS SENSING SYSTEM”, filed on Sep. 15, 2019,    -   (m) U.S. Provisional Patent application 62/902,357, entitled        “METHOD, APPARATUS, AND SYSTEM FOR AUTOMATIC AND OPTIMIZED        DEVICE-TO-CLOUD CONNECTION FOR WIRELESS SENSING”, filed on Sep.        18, 2019,    -   (n) U.S. patent application Ser. No. 16/667,648, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS PROXIMITY AND        PRESENCE MONITORING”, filed on Oct. 29, 2019,    -   (o) U.S. patent application Ser. No. 16/667,757, entitled        “METHOD, APPARATUS, AND SYSTEM FOR HUMAN IDENTIFICATION BASED ON        HUMAN RADIO BIOMETRIC INFORMATION”, filed on Oct. 29, 2019,    -   (p) U.S. Provisional Patent application 62/950,093, entitled        “METHOD, APPARATUS, AND SYSTEM FOR TARGET POSITIONING”, filed on        Dec. 18, 2019,    -   (q) U.S. patent application Ser. No. 16/790,610, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS GAIT RECOGNITION”,        filed Feb. 13, 2020,    -   (r) U.S. patent application Ser. No. 16/790,627, entitled        “METHOD, APPARATUS, AND SYSTEM FOR OUTDOOR TARGET TRACKING”,        filed Feb. 13, 2020.    -   (s) U.S. Provisional Patent application 62/977,326, entitled        “METHOD, APPARATUS, AND SYSTEM FOR AUTOMATIC AND ADAPTIVE        WIRELESS MONITORING AND TRACKING”, filed on Feb. 16, 2020,    -   (t) U.S. patent application Ser. No. 16/798,337, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS OBJECT SCANNING”,        filed Feb. 22, 2020,    -   (u) U.S. patent application Ser. No. 16/798,343, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS OBJECT TRACKING”,        filed Feb. 22, 2020,    -   (v) U.S. Provisional Patent application 62/980,206, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS SENSING”, filed on        Feb. 22, 2020,    -   (w) U.S. Provisional Patent application 62/981,387, entitled        “METHOD, APPARATUS, AND SYSTEM FOR VEHICLE WIRELESS MONITORING”,        filed on Feb. 25, 2020,    -   (x) U.S. Provisional Patent application 62/984,737, entitled        “METHOD, APPARATUS, AND SYSTEM FOR IMPROVED WIRELESS        MONITORING”, filed on Mar. 3, 2020,    -   (y) U.S. Provisional Patent application 63/001,226, entitled        “METHOD, APPARATUS, AND SYSTEM FOR IMPROVED WIRELESS MONITORING        AND USER INTERFACE”, filed on Mar. 27, 2020.

TECHNICAL FIELD

The present teaching generally relates wireless artificial intelligentapplications. More specifically, the present teaching relates to monitorbreathing rate, count number of people, and identify people in arich-scattering environment.

BACKGROUND

Human-centric sensing via wireless radio frequency (RF) has attracted anincreasing interest for a range of Internet of Things (IoT)applications. Demands of accurate and passive awareness of theenvironment surge for many applications. For instance, a smart home canadjust the light and ventilation system based on occupancy level toimprove energy efficiency. People recognition in smart homes enablesuser authentication for home security and privacy protection. Besidesunderstanding the environment, monitoring the status of human in theenvironment also has received great attention, and respiration/breathingrate, serving as a significant vital sign, has been an important topicfor RF sensing.

Compared with conventional methods that use dedicated sensors to monitorbreathing rates, RF sensing provides contact-free solutions. TheRF-based solutions can be classified into two categories, i.e.,radar-based methods and WiFi-based methods. For radar-based methods,previous works have shown the potential of using millimeter wave(mmWave) or ultra-wideband (UWB) to monitor respiration rate as well asheart rate. Although these systems can achieve high accuracy, dedicateddevices hinder their deployment. WiFi-based methods, on the contrary,can be easily deployed and have been studied in the past decade.Received signal strength (RSS) measured by a WiFi device has been usedto measure the chest movement during breathing. However, the accuracy ofrespiration rate estimation degrades when the test subjects do not holdthe device. Fine-grained channel state information (CSI) is moresensitive to the environment changes, which has been utilized to captureminute movements caused by respiration. However, due to theomni-directional propagation and narrow bandwidth of commonly used 2.4/5GHz WiFi, the received signal can be reflected from multiple humans inan indoor space. This makes it difficult to extract the vital signs ofmultiple humans from the reflected signal. Most of the existing worksassume that there is only one person in the observation area or assumethe respiration rates of different people are distinct and the number ofpeople is known in advance.

SUMMARY

The present teaching generally relates wireless artificial intelligent.More specifically, the present teaching relates to monitor breathingrate, count number of people, and identify people in a rich-scatteringenvironment, e.g. an indoor environment or urban metropolitan area,enclosed environment, a car, etc. In one embodiment, the presentteaching relates to accurately identify and recognize the driver basedon wireless channel information, monitor driver's and passengers'breathing rate, count number of passengers, and detect presence ofunattended children in a rich-scattering environment, e.g. an indoorenvironment or urban metropolitan area, enclosed environment, a car,etc.

In one embodiment, a system for rhythmic motion monitoring is described.The system comprises: a transmitter, a receiver, and a processor. Thetransmitter is configured for transmitting a wireless signal through awireless multipath channel that is impacted by a rhythmic motion of anobject in a venue. The receiver is configured for: receiving thewireless signal through the wireless multipath channel, and extractingN1 time series of channel information (TSCI) of the wireless multipathchannel from the wireless signal modulated by the wireless multipathchannel that is impacted by the rhythmic motion of the object, whereineach of the N1 TSCI is associated with an antenna of the transmitter andan antenna of the receiver. The processor is configured for: decomposingeach of the N1 TSCI into N2 time series of channel informationcomponents (TSCIC), wherein a channel information component (CIC) ofeach of the N2 TSCIC at a time comprises a respective component of achannel information (CI) of the TSCI at the time, monitoring therhythmic motion of the object based on at least one of: the N1*N2 TSCICand the N1 TSCI, wherein N1 and N2 are positive integers, and triggeringa response action based on the monitoring of the rhythmic motion of theobject.

In another embodiment, a method, implemented by a processor, a memorycommunicatively coupled with the processor, and a set of instructionsstored in the memory to be executed by the processor, is described. Themethod comprises: obtaining N1 time series of channel information (TSCI)of a wireless multipath channel that is impacted by a rhythmic motion ofan object in a venue, wherein the N1 TSCI is extracted from a wirelesssignal transmitted from a transmitter to a receiver through the wirelessmultipath channel, wherein each of the N1 TSCI is associated with anantenna of the transmitter and an antenna of the receiver; decomposingeach of the N1 TSCI into N2 time series of channel informationcomponents (TSCIC), wherein a channel information component (CIC) ofeach of the N2 TSCIC at a time comprises a respective component of achannel information (CI) of the TSCI at the time, wherein N1 and N2 arepositive integers; monitoring the rhythmic motion of the object based onat least one of: the N1*N2 TSCIC and the N1 TSCI; and triggering aresponse action based on the monitoring of the rhythmic motion of theobject.

In a different embodiment, an apparatus is disclosed for rhythmic motionmonitoring in a venue where a transmitter and a receiver are located.The apparatus comprises: at least one of the transmitter and thereceiver, and a processor. The transmitter is configured fortransmitting a wireless signal through a wireless multipath channel thatis impacted by a rhythmic motion of an object in the venue. The receiveris configured for receiving the wireless signal through the wirelessmultipath channel, and extracting N1 time series of channel information(TSCI) of the wireless multipath channel from the wireless signalmodulated by the wireless multipath channel that is impacted by therhythmic motion of the object. Each of the N1 TSCI is associated with anantenna of the transmitter and an antenna of the receiver. The processoris configured for: decomposing each of the N1 TSCI into N2 time seriesof channel information components (TSCIC), wherein a channel informationcomponent (CIC) of each of the N2 TSCIC at a time comprises a respectivecomponent of a channel information (CI) of the TSCI at the time,monitoring the rhythmic motion of the object based on at least one of:the N1*N2 TSCIC and the N1 TSCI, wherein N1 and N2 are positiveintegers, and triggering a response action based on the monitoring ofthe rhythmic motion of the object.

Other concepts relate to software for implementing the present teachingon wirelessly monitor breathing rate, count number of people, andidentify people in a rich-scattering environment. Additional novelfeatures will be set forth in part in the description which follows, andin part will become apparent to those skilled in the art uponexamination of the following and the accompanying drawings or may belearned by production or operation of the examples. The novel featuresof the present teachings may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF DRAWINGS

The methods, systems, and/or devices described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings.

FIG. 1 illustrates an exemplary an exemplary processing flow of thesystem, according to various embodiments of the present teaching.

FIG. 2 illustrates an exemplary power spectrum density of the CIRamplitude of 6 links with a scenario that 3 people sit in car withbreathing rate [10 14 15] bpm, according to various embodiments of thepresent teaching.

FIG. 3 illustrates an exemplary normalized power spectrum density ofdifferent links after subcarrier (SC) combination with a scenario that 3people sit in car with breathing rate [10 14 15] bpm, according tovarious embodiments of the present teaching.

FIG. 4 illustrates an exemplary power spectrum density after linkcombination when 3 people sit in car with breathing rate [10 14 15] bpm,according to various embodiments of the present teaching.

FIG. 5 illustrates an exemplary spectrum after link combination with ascenario that 3 people sit in car doing natural breathing and theiraverage breathing rate are [10 14 15] bpm, according to variousembodiments of the present teaching.

FIGS. 6A-6C illustrate an exemplary procedure of iterative dynamicprogramming (IDP), according to various embodiments of the presentteaching.

FIGS. 7A-7D illustrate exemplary traces found by IDP in four adjacenttime windows, according to various embodiments of the present teaching.

FIG. 8 illustrates an exemplary trace concatenation result of windowscorresponding to FIGS. 7A-7D, according to various embodiments of thepresent teaching.

FIG. 9 illustrates an exemplary effect of quasi-bilateral filter inpeople counting, according to various embodiments of the presentteaching.

FIG. 10 illustrates an exemplary estimated probability density function(PDF) of 4 testing subjects for people recognition, according to variousembodiments of the present teaching.

FIG. 11A illustrates an exemplary confusion matrix of a disclosed systemfor people counting in a lab, according to various embodiments of thepresent teaching.

FIG. 11B illustrates an exemplary confusion matrix of another system forpeople counting in a lab, according to various embodiments of thepresent teaching.

FIG. 11C illustrates an exemplary confusion matrix of a disclosed systemwithout quasi-bilateral filter enabled for people counting in a lab,according to various embodiments of the present teaching.

FIG. 12A illustrates an exemplary confusion matrix of a disclosed systemfor people counting in a car, according to various embodiments of thepresent teaching.

FIG. 12B illustrates an exemplary confusion matrix of another system forpeople counting in a car, according to various embodiments of thepresent teaching.

FIG. 12C illustrates an exemplary confusion matrix of a disclosed systemwithout quasi-bilateral filter enabled for people counting in a car,according to various embodiments of the present teaching.

FIG. 13 illustrates an exemplary confusion matrix of people recognitionin a smart home scenario, according to various embodiments of thepresent teaching.

FIG. 14 illustrates an experiment setup for resolution investigation,where 3 users sit at different spatial separations and with differentbreathing frequency separations, according to various embodiments of thepresent teaching.

FIG. 15 illustrates an exemplary set of experiment results, according tovarious embodiments of the present teaching.

FIG. 16 illustrates an exemplary iterative dynamic programmingalgorithm, according to various embodiments of the present teaching.

FIG. 17 illustrates an exemplary trace concatenating algorithm,according to various embodiments of the present teaching.

FIG. 18 illustrates an exemplary day view showing separate instances ofsleep, according to some embodiments of the present teaching.

FIG. 19A and FIG. 19B illustrate exemplary weekly views showing a 24hour scale, according to some embodiments of the present teaching.

FIG. 20A and FIG. 20B illustrate exemplary home views showing real-timebreathing rate and movement index, according to some embodiments of thepresent teaching.

FIGS. 21-27 illustrate more exemplary views of lifelog display,according to some embodiments of the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

In one embodiment, the present teaching discloses a method, apparatus,device, system, and/or software(method/apparatus/device/system/software) of a wireless monitoringsystem. A time series of channel information (CI) of a wirelessmultipath channel (channel) may be obtained (e.g. dynamically) using aprocessor, a memory communicatively coupled with the processor and a setof instructions stored in the memory. The time series of CI (TSCI) maybe extracted from a wireless signal (signal) transmitted between a Type1 heterogeneous wireless device (e.g. wireless transmitter, TX) and aType 2 heterogeneous wireless device (e.g. wireless receiver, RX) in avenue through the channel. The channel may be impacted by an expression(e.g. motion, movement, expression, and/or change inposition/pose/shape/expression) of an object in the venue. Acharacteristics and/or a spatial-temporal information (STI, e.g. motioninformation) of the object and/or of the motion of the object may bemonitored based on the TSCI. A task may be performed based on thecharacteristics and/or STI. A presentation associated with the task maybe generated in a user-interface (UI) on a device of a user. The TSCImay be a wireless signal stream. The TSCI or each CI may bepreprocessed. A device may be a station (STA). The symbol “A/B” means “Aand/or B” in the present teaching.

The expression may comprise placement, placement of moveable parts,location, position, orientation, identifiable place, region, spatialcoordinate, presentation, state, static expression, size, length, width,height, angle, scale, shape, curve, surface, area, volume, pose,posture, manifestation, body language, dynamic expression, motion,motion sequence, gesture, extension, contraction, distortion,deformation, body expression (e.g. head, face, eye, mouth, tongue, hair,voice, neck, limbs, arm, hand, leg, foot, muscle, moveable parts),surface expression (e.g. shape, texture, material, color,electromagnetic (EM) characteristics, visual pattern, wetness,reflectance, translucency, flexibility), material property (e.g. livingtissue, hair, fabric, metal, wood, leather, plastic, artificialmaterial, solid, liquid, gas, temperature), movement, activity,behavior, change of expression, and/or some combination.

The wireless signal may comprise: transmitted/received signal, EMradiation, RF signal/transmission, signal in licensed/unlicensed/ISMband, bandlimited signal, baseband signal, wireless/mobile/cellularcommunication signal, wireless/mobile/cellular network signal, meshsignal, light signal/communication, downlink/uplink signal,unicast/multicast/broadcast signal, standard (e.g. WLAN, WWAN, WPAN,WBAN, international, national, industry, defacto, IEEE, IEEE 802,802.11/15/16, WiFi, 802.11n/ac/ax/be, 3G/4G/LTE/5G/6G/7G/8G, 3GPP,Bluetooth, BLE, Zigbee, RFID, UWB, WiMax) compliant signal, protocolsignal, standard frame,beacon/pilot/probe/enquiry/acknowledgement/handshake/synchronizationsignal, management/control/data frame, management/control/data signal,standardized wireless/cellular communication protocol, reference signal,source signal, motion probe/detection/sensing signal, and/or series ofsignals. The wireless signal may comprise a line-of-sight (LOS), and/ora non-LOS component (or path/link). Each CI may beextracted/generated/computed/sensed at a layer (e.g. PHY/MAC layer inOSI model) of Type 2 device and may be obtained by an application (e.g.software, firmware, driver, app, wireless monitoring software/system).

The wireless multipath channel may comprise: a communication channel,analog frequency channel (e.g. with analog carrier frequency near700/800/900 MHz, 1.8/1.8/2.4/3/5/6/27/60 GHz), coded channel (e.g. inCDMA), and/or channel of a wireless network/system (e.g. WLAN, WiFi,mesh, LTE, 4G/5G, Bluetooth, Zigbee, UWB, RFID, microwave). It maycomprise more than one channel. The channels may be consecutive (e.g.with adjacent/overlapping bands) or non-consecutive channels (e.g.non-overlapping WiFi channels, one at 2.4 GHz and one at 5 GHz).

The TSCI may be extracted from the wireless signal at a layer of theType 2 device (e.g. a layer of OSI reference model, physical layer, datalink layer, logical link control layer, media access control (MAC)layer, network layer, transport layer, session layer, presentationlayer, application layer, TCP/IP layer, internet layer, link layer). TheTSCI may be extracted from a derived signal (e.g. baseband signal,motion detection signal, motion sensing signal) derived from thewireless signal (e.g. RF signal). It may be (wireless) measurementssensed by the communication protocol (e.g. standardized protocol) usingexisting mechanism (e.g. wireless/cellular communicationstandard/network, 3G/LTE/4G/5G/6G/7G/8G, WiFi, IEEE 802.11/15/16). Thederived signal may comprise a packet with at least one of: a preamble, aheader and a payload (e.g. for data/control/management in wirelesslinks/networks). The TSCI may be extracted from a probe signal (e.g.training sequence, STF, LTF, L-STF, L-LTF, L-SIG, HE-STF, HE-LTF,HE-SIG-A, HE-SIG-B, CEF) in the packet. A motion detection/sensingsignal may be recognized/identified base on the probe signal. The packetmay be a standard-compliant protocol frame, management frame, controlframe, data frame, sounding frame, excitation frame, illumination frame,null data frame, beacon frame, pilot frame, probe frame, request frame,response frame, association frame, reassociation frame, disassociationframe, authentication frame, action frame, report frame, poll frame,announcement frame, extension frame, enquiry frame, acknowledgementframe, RTS frame, CTS frame, QoS frame, CF-Poll frame, CF-Ack frame,block acknowledgement frame, reference frame, training frame, and/orsynchronization frame.

The packet may comprise a control data and/or a motion detection probe.A data (e.g. ID/parameters/characteristics/settings/controlsignal/command/instruction/notification/broadcasting-related informationof the Type 1 device) may be obtained from the payload. The wirelesssignal may be transmitted by the Type 1 device. It may be received bythe Type 2 device. A database (e.g. in local server, hub device, cloudserver, storage network) may be used to store the TSCI, characteristics,STI, signatures, patterns, behaviors, trends, parameters, analytics,output responses, identification information, user information, deviceinformation, channel information, venue (e.g. map, environmental model,network, proximity devices/networks) information, task information,class/category information, presentation (e.g. UI) information, and/orother information.

The Type 1/Type 2 device may comprise at least one of: electronics,circuitry, transmitter (TX)/receiver (RX)/transceiver, RF interface,“Origin Satellite”/“Tracker Bot”, unicast/multicast/broadcasting device,wireless source device, source/destination device, wireless node, hubdevice, target device, motion detection device, sensor device,remote/wireless sensor device, wireless communication device,wireless-enabled device, standard compliant device, and/or receiver. TheType 1 (or Type 2) device may be heterogeneous because, when there aremore than one instances of Type 1 (or Type 2) device, they may havedifferent circuitry, enclosure, structure, purpose, auxiliaryfunctionality, chip/IC, processor, memory, software, firmware, networkconnectivity, antenna, brand, model, appearance, form, shape, color,material, and/or specification. The Type 1/Type 2 device may comprise:access point, router, mesh router, internet-of-things (IoT) device,wireless terminal, one or more radio/RF subsystem/wireless interface(e.g. 2.4 GHz radio, 5 GHz radio, front haul radio, backhaul radio),modem, RF front end, RF/radio chip or integrated circuit (IC).

At least one of: Type 1 device, Type 2 device, a link between them, theobject, the characteristics, the STI, the monitoring of the motion, andthe task may be associated with an identification (ID) such as UUID. TheType 1/Type 2/another device mayobtain/store/retrieve/access/preprocess/condition/process/analyze/monitor/applythe TSCI. The Type 1 and Type 2 devices may communicate network trafficin another channel (e.g. Ethernet, HDMI, USB, Bluetooth, BLE, WiFi, LTE,other network, the wireless multipath channel) in parallel to thewireless signal. The Type 2 device may passively observe/monitor/receivethe wireless signal from the Type 1 device in the wireless multipathchannel without establishing connection (e.g.association/authentication) with, or requesting service from, the Type 1device.

The transmitter (i.e. Type 1 device) may function as (play role of)receiver (i.e. Type 2 device) temporarily, sporadically, continuously,repeatedly, interchangeably, alternately, simultaneously, concurrently,and/or contemporaneously; and vice versa. A device may function as Type1 device (transmitter) and/or Type 2 device (receiver) temporarily,sporadically, continuously, repeatedly, simultaneously, concurrently,and/or contemporaneously. There may be multiple wireless nodes eachbeing Type 1 (TX) and/or Type 2 (RX) device. A TSCI may be obtainedbetween every two nodes when they exchange/communicate wireless signals.The characteristics and/or STI of the object may be monitoredindividually based on a TSCI, or jointly based on two or more (e.g. all)TSCI. The motion of the object may be monitored actively (in that Type 1device, Type 2 device, or both, are wearable of/associated with theobject) and/or passively (in that both Type 1 and Type 2 devices are notwearable of/associated with the object). It may be passive because theobject may not be associated with the Type 1 device and/or the Type 2device. The object (e.g. user, an automated guided vehicle or AGV) maynot need to carry/install any wearables/fixtures (i.e. the Type 1 deviceand the Type 2 device are not wearable/attached devices that the objectneeds to carry in order perform the task). It may be active because theobject may be associated with either the Type 1 device and/or the Type 2device. The object may carry (or installed) a wearable/a fixture (e.g.the Type 1 device, the Type 2 device, a device communicatively coupledwith either the Type 1 device or the Type 2 device).

The presentation may be visual, audio, image, video, animation,graphical presentation, text, etc. A computation of the task may beperformed by a processor (or logic unit) of the Type 1 device, aprocessor (or logic unit) of an IC of the Type 1 device, a processor (orlogic unit) of the Type 2 device, a processor of an IC of the Type 2device, a local server, a cloud server, a data analysis subsystem, asignal analysis subsystem, and/or another processor. The task may beperformed with/without reference to a wireless fingerprint or a baseline(e.g. collected, processed, computed, transmitted and/or stored in atraining phase/survey/current survey/previous survey/recentsurvey/initial wireless survey, a passive fingerprint), a training, aprofile, a trained profile, a static profile, a survey, an initialwireless survey, an initial setup, an installation, a re-training, anupdating and a reset.

The Type 1 device (TX device) may comprise at least one heterogeneouswireless transmitter. The Type 2 device (RX device) may comprise atleast one heterogeneous wireless receiver. The Type 1 device and theType 2 device may be collocated. The Type 1 device and the Type 2 devicemay be the same device. Any device may have a data processingunit/apparatus, a computing unit/system, a network unit/system, aprocessor (e.g. logic unit), a memory communicatively coupled with theprocessor, and a set of instructions stored in the memory to be executedby the processor. Some processors, memories and sets of instructions maybe coordinated. There may be multiple Type 1 devices interacting (e.g.communicating, exchange signal/control/notification/other data) with thesame Type 2 device (or multiple Type 2 devices), and/or there may bemultiple Type 2 devices interacting with the same Type 1 device. Themultiple Type 1 devices/Type 2 devices may be synchronized and/orasynchronous, with same/different window width/size and/or time shift,same/different synchronized start time, synchronized end time, etc.Wireless signals sent by the multiple Type 1 devices may be sporadic,temporary, continuous, repeated, synchronous, simultaneous, concurrent,and/or contemporaneous. The multiple Type 1 devices/Type 2 devices mayoperate independently and/or collaboratively. A Type 1 and/or Type 2device may have/comprise/be heterogeneous hardware circuitry (e.g. aheterogeneous chip or a heterogeneous IC capable of generating/receivingthe wireless signal, extracting CI from received signal, or making theCI available). They may be communicatively coupled to same or differentservers (e.g. cloud server, edge server, local server, hub device).

Operation of one device may be based on operation, state, internalstate, storage, processor, memory output, physical location, computingresources, network of another device. Difference devices may communicatedirectly, and/or via another device/server/hub device/cloud server. Thedevices may be associated with one or more users, with associatedsettings. The settings may be chosen once, pre-programmed, and/orchanged (e.g. adjusted, varied, modified)/varied over time. There may beadditional steps in the method. The steps and/or the additional steps ofthe method may be performed in the order shown or in another order. Anysteps may be performed in parallel, iterated, or otherwise repeated orperformed in another manner. A user may be human, adult, older adult,man, woman, juvenile, child, baby, pet, animal, creature, machine,computer module/software, etc.

In the case of one or multiple Type 1 devices interacting with one ormultiple Type 2 devices, any processing (e.g. time domain, frequencydomain) may be different for different devices. The processing may bebased on locations, orientation, direction, roles, user-relatedcharacteristics, settings, configurations, available resources,available bandwidth, network connection, hardware, software, processor,co-processor, memory, battery life, available power, antennas, antennatypes, directional/unidirectional characteristics of the antenna, powersetting, and/or other parameters/characteristics of the devices.

The wireless receiver (e.g. Type 2 device) may receive the signal and/oranother signal from the wireless transmitter (e.g. Type 1 device). Thewireless receiver may receive another signal from another wirelesstransmitter (e.g. a second Type 1 device). The wireless transmitter maytransmit the signal and/or another signal to another wireless receiver(e.g. a second Type 2 device). The wireless transmitter, wirelessreceiver, another wireless receiver and/or another wireless transmittermay be moving with the object and/or another object. The another objectmay be tracked.

The Type 1 and/or Type 2 device may be capable of wirelessly couplingwith at least two Type 2 and/or Type 1 devices. The Type 1 device may becaused/controlled to switch/establish wireless coupling (e.g.association, authentication) from the Type 2 device to a second Type 2device at another location in the venue. Similarly, the Type 2 devicemay be caused/controlled to switch/establish wireless coupling from theType 1 device to a second Type 1 device at yet another location in thevenue. The switching may be controlled by a server (or a hub device),the processor, the Type 1 device, the Type 2 device, and/or anotherdevice. The radio used before and after switching may be different. Asecond wireless signal (second signal) may be caused to be transmittedbetween the Type 1 device and the second Type 2 device (or between theType 2 device and the second Type 1 device) through the channel. Asecond TSCI of the channel extracted from the second signal may beobtained. The second signal may be the first signal. Thecharacteristics, STI and/or another quantity of the object may bemonitored based on the second TSCI. The Type 1 device and the Type 2device may be the same. The characteristics, STI and/or another quantitywith different time stamps may form a waveform. The waveform may bedisplayed in the presentation.

The wireless signal and/or another signal may have data embedded. Thewireless signal may be a series of probe signals (e.g. a repeatedtransmission of probe signals, a re-use of one or more probe signals).The probe signals may change/vary over time. A probe signal may be astandard compliant signal, protocol signal, standardized wirelessprotocol signal, control signal, data signal, wireless communicationnetwork signal, cellular network signal, WiFi signal, LTE/5G/6G/7Gsignal, reference signal, beacon signal, motion detection signal, and/ormotion sensing signal. A probe signal may be formatted according to awireless network standard (e.g. WiFi), a cellular network standard (e.g.LTE/5G/6G), or another standard. A probe signal may comprise a packetwith a header and a payload. A probe signal may have data embedded. Thepayload may comprise data. A probe signal may be replaced by a datasignal. The probe signal may be embedded in a data signal. The wirelessreceiver, wireless transmitter, another wireless receiver and/or anotherwireless transmitter may be associated with at least one processor,memory communicatively coupled with respective processor, and/orrespective set of instructions stored in the memory which when executedcause the processor to perform any and/or all steps needed to determinethe STI (e.g. motion information), initial STI, initial time, direction,instantaneous location, instantaneous angle, and/or speed, of theobject. The processor, the memory and/or the set of instructions may beassociated with the Type 1 device, one of the at least one Type 2device, the object, a device associated with the object, another deviceassociated with the venue, a cloud server, a hub device, and/or anotherserver.

The Type 1 device may transmit the signal in a broadcasting manner to atleast one Type 2 device(s) through the channel in the venue. The signalis transmitted without the Type 1 device establishing wirelessconnection (e.g. association, authentication) with any Type 2 device,and without any Type 2 device requesting services from the Type 1device. The Type 1 device may transmit to a particular media accesscontrol (MAC) address common for more than one Type 2 devices. Each Type2 device may adjust its MAC address to the particular MAC address. Theparticular MAC address may be associated with the venue. The associationmay be recorded in an association table of an Association Server (e.g.hub device). The venue may be identified by the Type 1 device, a Type 2device and/or another device based on the particular MAC address, theseries of probe signals, and/or the at least one TSCI extracted from theprobe signals. For example, a Type 2 device may be moved to a newlocation in the venue (e.g. from another venue). The Type 1 device maybe newly set up in the venue such that the Type 1 and Type 2 devices arenot aware of each other. During set up, the Type 1 device may beinstructed/guided/caused/controlled (e.g. using dummy receiver, usinghardware pin setting/connection, using stored setting, using localsetting, using remote setting, using downloaded setting, using hubdevice, or using server) to send the series of probe signals to theparticular MAC address. Upon power up, the Type 2 device may scan forprobe signals according to a table of MAC addresses (e.g. stored in adesignated source, server, hub device, cloud server) that may be usedfor broadcasting at different locations (e.g. different MAC address usedfor different venue such as house, office, enclosure, floor,multi-storey building, store, airport, mall, stadium, hall, station,subway, lot, area, zone, region, district, city, country, continent).When the Type 2 device detects the probe signals sent to the particularMAC address, the Type 2 device can use the table to identify the venuebased on the MAC address. A location of a Type 2 device in the venue maybe computed based on the particular MAC address, the series of probesignals, and/or the at least one TSCI obtained by the Type 2 device fromthe probe signals. The computing may be performed by the Type 2 device.The particular MAC address may be changed (e.g. adjusted, varied,modified) over time. It may be changed according to a time table, rule,policy, mode, condition, situation and/or change. The particular MACaddress may be selected based on availability of the MAC address, apre-selected list, collision pattern, traffic pattern, data trafficbetween the Type 1 device and another device, effective bandwidth,random selection, and/or a MAC address switching plan. The particularMAC address may be the MAC address of a second wireless device (e.g. adummy receiver, or a receiver that serves as a dummy receiver).

The Type 1 device may transmit the probe signals in a channel selectedfrom a set of channels. At least one CI of the selected channel may beobtained by a respective Type 2 device from the probe signal transmittedin the selected channel. The selected channel may be changed (e.g.adjusted, varied, modified) over time. The change may be according to atime table, rule, policy, mode, condition, situation, and/or change. Theselected channel may be selected based on availability of channels,random selection, a pre-selected list, co-channel interference,inter-channel interference, channel traffic pattern, data trafficbetween the Type 1 device and another device, effective bandwidthassociated with channels, security criterion, channel switching plan, acriterion, a quality criterion, a signal quality condition, and/orconsideration.

The particular MAC address and/or an information of the selected channelmay be communicated between the Type 1 device and a server (e.g. hubdevice) through a network. The particular MAC address and/or theinformation of the selected channel may also be communicated between aType 2 device and a server (e.g. hub device) through another network.The Type 2 device may communicate the particular MAC address and/or theinformation of the selected channel to another Type 2 device (e.g. viamesh network, Bluetooth, WiFi, NFC, ZigBee, etc.). The particular MACaddress and/or selected channel may be chosen by a server (e.g. hubdevice). The particular MAC address and/or selected channel may besignaled in an announcement channel by the Type 1 device, the Type 2device and/or a server (e.g. hub device). Before being communicated, anyinformation may be pre-processed.

Wireless connection (e.g. association, authentication) between the Type1 device and another wireless device may be established (e.g. using asignal handshake). The Type 1 device may send a first handshake signal(e.g. sounding frame, probe signal, request-to-send RTS) to the anotherdevice. The another device may reply by sending a second handshakesignal (e.g. a command, or a clear-to-send CTS) to the Type 1 device,triggering the Type 1 device to transmit the signal (e.g. series ofprobe signals) in the broadcasting manner to multiple Type 2 deviceswithout establishing connection with any Type 2 device. The secondhandshake signals may be a response or an acknowledge (e.g. ACK) to thefirst handshake signal. The second handshake signal may contain a datawith information of the venue, and/or the Type 1 device. The anotherdevice may be a dummy device with a purpose (e.g. primary purpose,secondary purpose) to establish the wireless connection with the Type 1device, to receive the first signal, and/or to send the second signal.The another device may be physically attached to the Type 1 device.

In another example, the another device may send a third handshake signalto the Type 1 device triggering the Type 1 device to broadcast thesignal (e.g. series of probe signals) to multiple Type 2 devices withoutestablishing connection (e.g. association, authentication) with any Type2 device. The Type 1 device may reply to the third special signal bytransmitting a fourth handshake signal to the another device. Theanother device may be used to trigger more than one Type 1 devices tobroadcast. The triggering may be sequential, partially sequential,partially parallel, or fully parallel. The another device may have morethan one wireless circuitries to trigger multiple transmitters inparallel. Parallel trigger may also be achieved using at least one yetanother device to perform the triggering (similar to what as the anotherdevice does) in parallel to the another device. The another device maynot communicate (or suspend communication) with the Type 1 device afterestablishing connection with the Type 1 device. Suspended communicationmay be resumed. The another device may enter an inactive mode,hibernation mode, sleep mode, stand-by mode, low-power mode, OFF modeand/or power-down mode, after establishing the connection with the Type1 device. The another device may have the particular MAC address so thatthe Type 1 device sends the signal to the particular MAC address. TheType 1 device and/or the another device may be controlled and/orcoordinated by a first processor associated with the Type 1 device, asecond processor associated with the another device, a third processorassociated with a designated source and/or a fourth processor associatedwith another device. The first and second processors may coordinate witheach other.

A first series of probe signals may be transmitted by a first antenna ofthe Type 1 device to at least one first Type 2 device through a firstchannel in a first venue. A second series of probe signals may betransmitted by a second antenna of the Type 1 device to at least onesecond Type 2 device through a second channel in a second venue. Thefirst series and the second series may/may not be different. The atleast one first Type 2 device may/may not be different from the at leastone second Type 2 device. The first and/or second series of probesignals may be broadcasted without connection (e.g. association,authentication) established between the Type 1 device and any Type 2device. The first and second antennas may be same/different. The twovenues may have different sizes, shape, multipath characteristics. Thefirst and second venues may overlap. The respective immediate areasaround the first and second antennas may overlap. The first and secondchannels may be same/different. For example, the first one may be WiFiwhile the second may be LTE. Or, both may be WiFi, but the first one maybe 2.4 GHz WiFi and the second may be 5 GHz WiFi. Or, both may be 2.4GHz WiFi, but have different channel numbers, SSID names, and/or WiFisettings.

Each Type 2 device may obtain at least one TSCI from the respectiveseries of probe signals, the CI being of the respective channel betweenthe Type 2 device and the Type 1 device. Some first Type 2 device(s) andsome second Type 2 device(s) may be the same. The first and secondseries of probe signals may be synchronous/asynchronous. A probe signalmay be transmitted with data or replaced by a data signal. The first andsecond antennas may be the same. The first series of probe signals maybe transmitted at a first rate (e.g. 30 Hz). The second series of probesignals may be transmitted at a second rate (e.g. 200 Hz). The first andsecond rates may be same/different. The first and/or second rate may bechanged (e.g. adjusted, varied, modified) over time. The change may beaccording to a time table, rule, policy, mode, condition, situation,and/or change. Any rate may be changed (e.g. adjusted, varied, modified)over time. The first and/or second series of probe signals may betransmitted to a first MAC address and/or second MAC addressrespectively. The two MAC addresses may be same/different. The firstseries of probe signals may be transmitted in a first channel. Thesecond series of probe signals may be transmitted in a second channel.The two channels may be same/different. The first or second MAC address,first or second channel may be changed over time. Any change may beaccording to a time table, rule, policy, mode, condition, situation,and/or change.

The Type 1 device and another device may be controlled and/orcoordinated, physically attached, or may be of/in/of a common device.They may be controlled by/connected to a common data processor, or maybe connected to a common bus interconnect/network/LAN/Bluetoothnetwork/NFC network/BLE network/wired network/wireless network/meshnetwork/mobile network/cloud. They may share a common memory, or beassociated with a common user, user device, profile, account, identity(ID), identifier, household, house, physical address, location,geographic coordinate, IP subnet, SSID, home device, office device,and/or manufacturing device. Each Type 1 device may be a signal sourceof a set of respective Type 2 devices (i.e. it sends a respective signal(e.g. respective series of probe signals) to the set of respective Type2 devices). Each respective Type 2 device chooses the Type 1 device fromamong all Type 1 devices as its signal source. Each Type 2 device maychoose asynchronously. At least one TSCI may be obtained by eachrespective Type 2 device from the respective series of probe signalsfrom the Type 1 device, the CI being of the channel between the Type 2device and the Type 1 device. The respective Type 2 device chooses theType 1 device from among all Type 1 devices as its signal source basedon identity (ID) or identifier of Type 1/Type 2 device, task to beperformed, past signal source, history (e.g. of past signal source, Type1 device, another Type 1 device, respective Type 2 receiver, and/oranother Type 2 receiver), threshold for switching signal source, and/orinformation of a user, account, access info, parameter, characteristics,and/or signal strength (e.g. associated with the Type 1 device and/orthe respective Type 2 receiver). Initially, the Type 1 device may besignal source of a set of initial respective Type 2 devices (i.e. theType 1 device sends a respective signal (series of probe signals) to theset of initial respective Type 2 devices) at an initial time. Eachinitial respective Type 2 device chooses the Type 1 device from amongall Type 1 devices as its signal source.

The signal source (Type 1 device) of a particular Type 2 device may bechanged (e.g. adjusted, varied, modified) when (1) time interval betweentwo adjacent probe signals (e.g. between current probe signal andimmediate past probe signal, or between next probe signal and currentprobe signal) received from current signal source of the Type 2 deviceexceeds a first threshold; (2) signal strength associated with currentsignal source of the Type 2 device is below a second threshold; (3) aprocessed signal strength associated with current signal source of theType 2 device is below a third threshold, the signal strength processedwith low pass filter, band pass filter, median filter, moving averagefilter, weighted averaging filter, linear filter and/or non-linearfilter; and/or (4) signal strength (or processed signal strength)associated with current signal source of the Type 2 device is below afourth threshold for a significant percentage of a recent time window(e.g. 70%, 80%, 90%). The percentage may exceed a fifth threshold. Thefirst, second, third, fourth and/or fifth thresholds may be timevarying.

Condition (1) may occur when the Type 1 device and the Type 2 devicebecome progressively far away from each other, such that some probesignal from the Type 1 device becomes too weak and is not received bythe Type 2 device. Conditions (2)-(4) may occur when the two devicesbecome far from each other such that the signal strength becomes veryweak.

The signal source of the Type 2 device may not change if other Type 1devices have signal strength weaker than a factor (e.g. 1, 1.1, 1.2, or1.5) of the current signal source. If the signal source is changed (e.g.adjusted, varied, modified), the new signal source may take effect at anear future time (e.g. the respective next time). The new signal sourcemay be the Type 1 device with strongest signal strength, and/orprocessed signal strength. The current and new signal source may besame/different.

A list of available Type 1 devices may be initialized and maintained byeach Type 2 device. The list may be updated by examining signal strengthand/or processed signal strength associated with the respective set ofType 1 devices. A Type 2 device may choose between a first series ofprobe signals from a first Type 1 device and a second series of probesignals from a second Type 1 device based on: respective probe signalrate, MAC addresses, channels, characteristics/properties/states, taskto be performed by the Type 2 device, signal strength of first andsecond series, and/or another consideration.

The series of probe signals may be transmitted at a regular rate (e.g.100 Hz). The series of probe signals may be scheduled at a regularinterval (e.g. 0.01 s for 100 Hz), but each probe signal may experiencesmall time perturbation, perhaps due to timing requirement, timingcontrol, network control, handshaking, message passing, collisionavoidance, carrier sensing, congestion, availability of resources,and/or another consideration. The rate may be changed (e.g. adjusted,varied, modified). The change may be according to a time table (e.g.changed once every hour), rule, policy, mode, condition and/or change(e.g. changed whenever some event occur). For example, the rate maynormally be 100 Hz, but changed to 1000 Hz in demanding situations, andto 1 Hz in low power/standby situation. The probe signals may be sent inburst.

The probe signal rate may change based on a task performed by the Type 1device or Type 2 device (e.g. a task may need 100 Hz normally and 1000Hz momentarily for 20 seconds). In one example, the transmitters (Type 1devices), receivers (Type 2 device), and associated tasks may beassociated adaptively (and/or dynamically) to classes (e.g. classes thatare: low-priority, high-priority, emergency, critical, regular,privileged, non-subscription, subscription, paying, and/or non-paying).A rate (of a transmitter) may be adjusted for the sake of some class(e.g. high priority class). When the need of that class changes, therate may be changed (e.g. adjusted, varied, modified). When a receiverhas critically low power, the rate may be reduced to reduce powerconsumption of the receiver to respond to the probe signals. In oneexample, probe signals may be used to transfer power wirelessly to areceiver (Type 2 device), and the rate may be adjusted to control theamount of power transferred to the receiver.

The rate may be changed by (or based on): a server (e.g. hub device),the Type 1 device and/or the Type 2 device. Control signals may becommunicated between them. The server may monitor, track, forecastand/or anticipate the needs of the Type 2 device and/or the tasksperformed by the Type 2 device, and may control the Type 1 device tochange the rate. The server may make scheduled changes to the rateaccording to a time table. The server may detect an emergency situationand change the rate immediately. The server may detect a developingcondition and adjust the rate gradually. The characteristics and/or STI(e.g. motion information) may be monitored individually based on a TSCIassociated with a particular Type 1 device and a particular Type 2device, and/or monitored jointly based on any TSCI associated with theparticular Type 1 device and any Type 2 device, and/or monitored jointlybased on any TSCI associated with the particular Type 2 device and anyType 1 device, and/or monitored globally based on any TSCI associatedwith any Type 1 device and any Type 2 device. Any joint monitoring maybe associated with: a user, user account, profile, household, map ofvenue, environmental model of the venue, and/or user history, etc.

A first channel between a Type 1 device and a Type 2 device may bedifferent from a second channel between another Type 1 device andanother Type 2 device. The two channels may be associated with differentfrequency bands, bandwidth, carrier frequency, modulation, wirelessstandards, coding, encryption, payload characteristics, networks,network ID, SSID, network characteristics, network settings, and/ornetwork parameters, etc. The two channels may be associated withdifferent kinds of wireless system (e.g. two of the following: WiFi,LTE, LTE-A, LTE-U, 2.5G, 3G, 3.5G, 4G, beyond 4G, 5G, 6G, 7G, a cellularnetwork standard, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA,TD-SCDMA, 802.11 system, 802.15 system, 802.16 system, mesh network,Zigbee, NFC, WiMax, Bluetooth, BLE, RFID, UWB, microwave system, radarlike system). For example, one is WiFi and the other is LTE. The twochannels may be associated with similar kinds of wireless system, but indifferent network. For example, the first channel may be associated witha WiFi network named “Pizza and Pizza” in the 2.4 GHz band with abandwidth of 20 MHz while the second may be associated with a WiFinetwork with SSID of “StarBud hotspot” in the 5 GHz band with abandwidth of 40 MHz. The two channels may be different channels in samenetwork (e.g. the “StarBud hotspot” network).

In one embodiment, a wireless monitoring system may comprise training aclassifier of multiple events in a venue based on training TSCIassociated with the multiple events. A CI or TSCI associated with anevent may be considered/may comprise a wirelesssample/characteristics/fingerprint associated with the event (and/or thevenue, the environment, the object, the motion of the object, astate/emotional state/mentalstate/condition/stage/gesture/gait/action/movement/activity/dailyactivity/history/event of the object, etc.). For each of the multipleknown events happening in the venue in a respective training (e.g.surveying, wireless survey, initial wireless survey) time periodassociated with the known event, a respective training wireless signal(e.g. a respective series of training probe signals) may be transmittedby an antenna of a first Type 1 heterogeneous wireless device using aprocessor, a memory and a set of instructions of the first Type 1 deviceto at least one first Type 2 heterogeneous wireless device through awireless multipath channel in the venue in the respective training timeperiod.

At least one respective time series of training CI (training TSCI) maybe obtained asynchronously by each of the at least one first Type 2device from the (respective) training signal. The CI may be CI of thechannel between the first Type 2 device and the first Type 1 device inthe training time period associated with the known event. The at leastone training TSCI may be preprocessed. The training may be a wirelesssurvey (e.g. during installation of Type 1 device and/or Type 2 device).

For a current event happening in the venue in a current time period, acurrent wireless signal (e.g. a series of current probe signals) may betransmitted by an antenna of a second Type 1 heterogeneous wirelessdevice using a processor, a memory and a set of instructions of thesecond Type 1 device to at least one second Type 2 heterogeneouswireless device through the channel in the venue in the current timeperiod associated with the current event. At least one time series ofcurrent CI (current TSCI) may be obtained asynchronously by each of theat least one second Type 2 device from the current signal (e.g. theseries of current probe signals). The CI may be CI of the channelbetween the second Type 2 device and the second Type 1 device in thecurrent time period associated with the current event. The at least onecurrent TSCI may be preprocessed.

The classifier may be applied to classify at least one current TSCIobtained from the series of current probe signals by the at least onesecond Type 2 device, to classify at least one portion of a particularcurrent TSCI, and/or to classify a combination of the at least oneportion of the particular current TSCI and another portion of anotherTSCI. The classifier may partition TSCI (or the characteristics/STI orother analytics or output responses) into clusters and associate theclusters to specificevents/objects/subjects/locations/movements/activities. Labels/tags maybe generated for the clusters. The clusters may be stored and retrieved.The classifier may be applied to associate the current TSCI (orcharacteristics/STI or the other analytics/output response, perhapsassociated with a current event) with: a cluster, a known/specificevent, a class/category/group/grouping/list/cluster/set of knownevents/subjects/locations/movements/activities, an unknown event, aclass/category/group/grouping/list/cluster/set of unknownevents/subjects/locations/movements/activities, and/or anotherevent/subject/location/movement/activity/class/category/group/grouping/list/cluster/set.Each TSCI may comprise at least one CI each associated with a respectivetimestamp. Two TSCI associated with two Type 2 devices may be differentwith different: starting time, duration, stopping time, amount of CI,sampling frequency, sampling period. Their CI may have differentfeatures. The first and second Type 1 devices may be at same location inthe venue. They may be the same device. The at least one second Type 2device (or their locations) may be a permutation of the at least onefirst Type 2 device (or their locations). A particular second Type 2device and a particular first Type 2 device may be the same device. Asubset of the first Type 2 device and a subset of the second Type 2device may be the same. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be a subset of theat least one first Type 2 device. The at least one first Type 2 deviceand/or a subset of the at least one first Type 2 device may be apermutation of a subset of the at least one second Type 2 device. The atleast one second Type 2 device and/or a subset of the at least onesecond Type 2 device may be a permutation of a subset of the at leastone first Type 2 device. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be at samerespective location as a subset of the at least one first Type 2 device.The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be at same respective location as a subset ofthe at least one second Type 2 device.

The antenna of the Type 1 device and the antenna of the second Type 1device may be at same location in the venue. Antenna(s) of the at leastone second Type 2 device and/or antenna(s) of a subset of the at leastone second Type 2 device may be at same respective location asrespective antenna(s) of a subset of the at least one first Type 2device. Antenna(s) of the at least one first Type 2 device and/orantenna(s) of a subset of the at least one first Type 2 device may be atsame respective location(s) as respective antenna(s) of a subset of theat least one second Type 2 device.

A first section of a first time duration of the first TSCI and a secondsection of a second time duration of the second section of the secondTSCI may be aligned. A map between items of the first section and itemsof the second section may be computed. The first section may comprise afirst segment (e.g. subset) of the first TSCI with a firststarting/ending time, and/or another segment (e.g. subset) of aprocessed first TSCI. The processed first TSCI may be the first TSCIprocessed by a first operation. The second section may comprise a secondsegment (e.g. subset) of the second TSCI with a second starting time anda second ending time, and another segment (e.g. subset) of a processedsecond TSCI. The processed second TSCI may be the second TSCI processedby a second operation. The first operation and/or the second operationmay comprise: subsampling, re-sampling, interpolation, filtering,transformation, feature extraction, pre-processing, and/or anotheroperation.

A first item of the first section may be mapped to a second item of thesecond section. The first item of the first section may also be mappedto another item of the second section. Another item of the first sectionmay also be mapped to the second item of the second section. The mappingmay be one-to-one, one-to-many, many-to-one, many-to-many. At least onefunction of at least one of: the first item of the first section of thefirst TSCI, another item of the first TSCI, timestamp of the first item,time difference of the first item, time differential of the first item,neighboring timestamp of the first item, another timestamp associatedwith the first item, the second item of the second section of the secondTSCI, another item of the second TSCI, timestamp of the second item,time difference of the second item, time differential of the seconditem, neighboring timestamp of the second item, and another timestampassociated with the second item, may satisfy at least one constraint.

One constraint may be that a difference between the timestamp of thefirst item and the timestamp of the second item may be upper-bounded byan adaptive (and/or dynamically adjusted) upper threshold andlower-bounded by an adaptive lower threshold.

The first section may be the entire first TSCI. The second section maybe the entire second TSCI. The first time duration may be equal to thesecond time duration. A section of a time duration of a TSCI may bedetermined adaptively (and/or dynamically). A tentative section of theTSCI may be computed. A starting time and an ending time of a section(e.g. the tentative section, the section) may be determined. The sectionmay be determined by removing a beginning portion and an ending portionof the tentative section. A beginning portion of a tentative section maybe determined as follows. Iteratively, items of the tentative sectionwith increasing timestamp may be considered as a current item, one itemat a time.

In each iteration, at least one activity measure/index may be computedand/or considered. The at least one activity measure may be associatedwith at least one of: the current item associated with a currenttimestamp, past items of the tentative section with timestamps notlarger than the current timestamp, and/or future items of the tentativesection with timestamps not smaller than the current timestamp. Thecurrent item may be added to the beginning portion of the tentativesection if at least one criterion (e.g. quality criterion, signalquality condition) associated with the at least one activity measure issatisfied.

The at least one criterion associated with the activity measure maycomprise at least one of: (a) the activity measure is smaller than anadaptive (e.g. dynamically adjusted) upper threshold, (b) the activitymeasure is larger than an adaptive lower threshold, (c) the activitymeasure is smaller than an adaptive upper threshold consecutively for atleast a predetermined amount of consecutive timestamps, (d) the activitymeasure is larger than an adaptive lower threshold consecutively for atleast another predetermined amount of consecutive timestamps, (e) theactivity measure is smaller than an adaptive upper thresholdconsecutively for at least a predetermined percentage of thepredetermined amount of consecutive timestamps, (f) the activity measureis larger than an adaptive lower threshold consecutively for at leastanother predetermined percentage of the another predetermined amount ofconsecutive timestamps, (g) another activity measure associated withanother timestamp associated with the current timestamp is smaller thananother adaptive upper threshold and larger than another adaptive lowerthreshold, (h) at least one activity measure associated with at leastone respective timestamp associated with the current timestamp issmaller than respective upper threshold and larger than respective lowerthreshold, (i) percentage of timestamps with associated activity measuresmaller than respective upper threshold and larger than respective lowerthreshold in a set of timestamps associated with the current timestampexceeds a threshold, and (j) another criterion (e.g. a qualitycriterion, signal quality condition).

An activity measure/index associated with an item at time T1 maycomprise at least one of: (1) a first function of the item at time T1and an item at time T1−D1, wherein D1 is a pre-determined positivequantity (e.g. a constant time offset), (2) a second function of theitem at time T1 and an item at time T1+D1, (3) a third function of theitem at time T1 and an item at time T2, wherein T2 is a pre-determinedquantity (e.g. a fixed initial reference time; T2 may be changed (e.g.adjusted, varied, modified) over time; T2 may be updated periodically;T2 may be the beginning of a time period and T1 may be a sliding time inthe time period), and (4) a fourth function of the item at time T1 andanother item.

At least one of: the first function, the second function, the thirdfunction, and/or the fourth function may be a function (e.g. F(X, Y, . .. )) with at least two arguments: X and Y. The two arguments may bescalars. The function (e.g. F) may be a function of at least one of: X,Y, (X−Y), (Y−X), abs(X−Y), X{circumflex over ( )}a, Y{circumflex over( )}b, abs(X{circumflex over ( )}a−Y{circumflex over ( )}b),(X−Y){circumflex over ( )}a, (X/Y), (X+a)/(Y+b), (X{circumflex over( )}a/Y{circumflex over ( )}b), and ((X/Y){circumflex over ( )}a−b),wherein a and b are may be some predetermined quantities. For example,the function may simply be abs(X−Y), or (X−Y){circumflex over ( )}2,(X−Y){circumflex over ( )}4. The function may be a robust function. Forexample, the function may be (X−Y){circumflex over ( )}2 when abs(X−Y)is less than a threshold T, and (X−Y)+a when abs(X−Y) is larger than T.Alternatively, the function may be a constant when abs(X−Y) is largerthan T. The function may also be bounded by a slowly increasing functionwhen abs(X−y) is larger than T, so that outliers cannot severely affectthe result. Another example of the function may be (abs(X/Y)−a), wherea=1. In this way, if X=Y (i.e. no change or no activity), the functionwill give a value of 0. If X is larger than Y, (X/Y) will be larger than1 (assuming X and Y are positive) and the function will be positive. Andif X is less than Y, (X/Y) will be smaller than 1 and the function willbe negative. In another example, both arguments X and Y may be n-tuplessuch that X=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2, . . . , y_n). Thefunction may be a function of at least one of: x_i, y_i, (x_i−y_i),(y_i−x_i), abs(x_i−y_i), x_i{circumflex over ( )}a, y_i{circumflex over( )}b, abs(x_i{circumflex over ( )}a−y_i{circumflex over ( )}b),(x_i−y_i){circumflex over ( )}a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i{circumflex over ( )}a/y_i{circumflex over ( )}b), and((x_i/y_i){circumflex over ( )}a−b), wherein i is a component index ofthe n-tuple X and Y, and 1<=i<=n. E.g. component index of x_1 is i=1,component index of x_2 is i=2. The function may comprise acomponent-by-component summation of another function of at least one ofthe following: x_i, y_i, (x_i−y_i), (y_i−x_i), abs(x_i−y_i),x_i{circumflex over ( )}a, y_i{circumflex over ( )}b, abs(x_i{circumflexover ( )}a−y_i{circumflex over ( )}b), (x_i−y_i){circumflex over ( )}a,(x_i/y_i), (x_i+a)/(y_i+b), (x_i{circumflex over ( )}a/y_i{circumflexover ( )}b), and ((x_i/y_i){circumflex over ( )}a−b), wherein i is thecomponent index of the n-tuple X and Y. For example, the function may bein a form of sum_{i=1}{circumflex over ( )}n(abs(x_i/y_i)−1)/n, orsum_{i=1}{circumflex over ( )}n w_i*(abs(x_i/y_i)−1), where w_i is someweight for component i.

The map may be computed using dynamic time warping (DTW). The DTW maycomprise a constraint on at least one of: the map, the items of thefirst TSCI, the items of the second TSCI, the first time duration, thesecond time duration, the first section, and/or the second section.Suppose in the map, the i{circumflex over ( )}{th} domain item is mappedto the j{circumflex over ( )}{th} range item. The constraint may be onadmissible combination of i and j (constraint on relationship between iand j). Mismatch cost between a first section of a first time durationof a first TSCI and a second section of a second time duration of asecond TSCI may be computed.

The first section and the second section may be aligned such that a mapcomprising more than one links may be established between first items ofthe first TSCI and second items of the second TSCI. With each link, oneof the first items with a first timestamp may be associated with one ofthe second items with a second timestamp. A mismatch cost between thealigned first section and the aligned second section may be computed.The mismatch cost may comprise a function of: an item-wise cost betweena first item and a second item associated by a particular link of themap, and a link-wise cost associated with the particular link of themap.

The aligned first section and the aligned second section may berepresented respectively as a first vector and a second vector of samevector length. The mismatch cost may comprise at least one of: an innerproduct, inner-product-like quantity, quantity based on correlation,correlation indicator, quantity based on covariance, discriminatingscore, distance, Euclidean distance, absolute distance, Lk distance(e.g. L1, L2, . . . ), weighted distance, distance-like quantity and/oranother similarity value, between the first vector and the secondvector. The mismatch cost may be normalized by the respective vectorlength.

A parameter derived from the mismatch cost between the first section ofthe first time duration of the first TSCI and the second section of thesecond time duration of the second TSCI may be modeled with astatistical distribution. At least one of: a scale parameter, locationparameter and/or another parameter, of the statistical distribution maybe estimated. The first section of the first time duration of the firstTSCI may be a sliding section of the first TSCI. The second section ofthe second time duration of the second TSCI may be a sliding section ofthe second TSCI. A first sliding window may be applied to the first TSCIand a corresponding second sliding window may be applied to the secondTSCI. The first sliding window of the first TSCI and the correspondingsecond sliding window of the second TSCI may be aligned.

Mismatch cost between the aligned first sliding window of the first TSCIand the corresponding aligned second sliding window of the second TSCImay be computed. The current event may be associated with at least oneof: the known event, the unknown event and/or the another event, basedon the mismatch cost.

The classifier may be applied to at least one of: each first section ofthe first time duration of the first TSCI, and/or each second section ofthe second time duration of the second TSCI, to obtain at least onetentative classification results. Each tentative classification resultmay be associated with a respective first section and a respectivesecond section.

The current event may be associated with at least one of: the knownevent, the unknown event, a class/category/group/grouping/list/set ofunknown events, and/or the another event, based on the mismatch cost.The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on a largestnumber of tentative classification results in more than one sections ofthe first TSCI and corresponding more than sections of the second TSCI.For example, the current event may be associated with a particular knownevent if the mismatch cost points to the particular known event for Nconsecutive times (e.g. N=10). In another example, the current event maybe associated with a particular known event if the percentage ofmismatch cost within the immediate past N consecutive N pointing to theparticular known event exceeds a certain threshold (e.g. >80%). Inanother example, the current event may be associated with a known eventthat achieves smallest mismatch cost for the most times within a timeperiod. The current event may be associated with a known event thatachieves smallest overall mismatch cost, which is a weighted average ofat least one mismatch cost associated with the at least one firstsections. The current event may be associated with a particular knownevent that achieves smallest of another overall cost. The current eventmay be associated with the “unknown event” if none of the known eventsachieve mismatch cost lower than a first threshold T1 in a sufficientpercentage of the at least one first section. The current event may alsobe associated with the “unknown event” if none of the events achieve anoverall mismatch cost lower than a second threshold T2. The currentevent may be associated with at least one of: the known event, theunknown event and/or the another event, based on the mismatch cost andadditional mismatch cost associated with at least one additional sectionof the first TSCI and at least one additional section of the secondTSCI. The known events may comprise at least one of: a door closedevent, door open event, window closed event, window open event,multi-state event, on-state event, off-state event, intermediate stateevent, continuous state event, discrete state event, human-presentevent, human-absent event, sign-of-life-present event, and/or asign-of-life-absent event.

A projection for each CI may be trained using a dimension reductionmethod based on the training TSCI. The dimension reduction method maycomprise at least one of: principal component analysis (PCA), PCA withdifferent kernel, independent component analysis (ICA), Fisher lineardiscriminant, vector quantization, supervised learning, unsupervisedlearning, self-organizing maps, auto-encoder, neural network, deepneural network, and/or another method. The projection may be applied toat least one of: the training TSCI associated with the at least oneevent, and/or the current TSCI, for the classifier. The classifier ofthe at least one event may be trained based on the projection and thetraining TSCI associated with the at least one event. The at least onecurrent TSCI may be classified/categorized based on the projection andthe current TSCI. The projection may be re-trained using at least oneof: the dimension reduction method, and another dimension reductionmethod, based on at least one of: the training TSCI, at least onecurrent TSCI before retraining the projection, and/or additionaltraining TSCI. The another dimension reduction method may comprise atleast one of: principal component analysis (PCA), PCA with differentkernels, independent component analysis (ICA), Fisher lineardiscriminant, vector quantization, supervised learning, unsupervisedlearning, self-organizing maps, auto-encoder, neural network, deepneural network, and/or yet another method. The classifier of the atleast one event may be re-trained based on at least one of: there-trained projection, the training TSCI associated with the at leastone events, and/or at least one current TSCI. The at least one currentTSCI may be classified based on: the re-trained projection, there-trained classifier, and/or the current TSCI.

Each CI may comprise a vector of complex values. Each complex value maybe preprocessed to give the magnitude of the complex value. Each CI maybe preprocessed to give a vector of non-negative real numbers comprisingthe magnitude of corresponding complex values. Each training TSCI may beweighted in the training of the projection. The projection may comprisemore than one projected components. The projection may comprise at leastone most significant projected component. The projection may comprise atleast one projected component that may be beneficial for the classifier.

The channel information (CI) may be associated with/may comprise signalstrength, signal amplitude, signal phase, spectral power measurement,modem parameters (e.g. used in relation to modulation/demodulation indigital communication systems such as WiFi, 4G/LTE), dynamic beamforminginformation, transfer function components, radio state (e.g. used indigital communication systems to decode digital data, basebandprocessing state, RF processing state, etc.), measurable variables,sensed data, coarse-grained/fine-grained information of a layer (e.g.physical layer, data link layer, MAC layer, etc.), digital setting, gainsetting, RF filter setting, RF front end switch setting, DC offsetsetting, DC correction setting, IQ compensation setting, effect(s) onthe wireless signal by the environment (e.g. venue) during propagation,transformation of an input signal (the wireless signal transmitted bythe Type 1 device) to an output signal (the wireless signal received bythe Type 2 device), a stable behavior of the environment, a stateprofile, wireless channel measurements, received signal strengthindicator (RSSI), channel state information (CSI), channel impulseresponse (CIR), channel frequency response (CFR), characteristics offrequency components (e.g. subcarriers) in a bandwidth, channelcharacteristics, channel filter response, timestamp, auxiliaryinformation, data, meta data, user data, account data, access data,security data, session data, status data, supervisory data, householddata, identity (ID), identifier, device data, network data, neighborhooddata, environment data, real-time data, sensor data, stored data,encrypted data, compressed data, protected data, and/or another channelinformation. Each CI may be associated with a time stamp, and/or anarrival time. A CSI can be used to equalize/undo/minimize/reduce themultipath channel effect (of the transmission channel) to demodulate asignal similar to the one transmitted by the transmitter through themultipath channel. The CI may be associated with information associatedwith a frequency band, frequency signature, frequency phase, frequencyamplitude, frequency trend, frequency characteristics, frequency-likecharacteristics, time domain element, frequency domain element,time-frequency domain element, orthogonal decomposition characteristics,and/or non-orthogonal decomposition characteristics of the signalthrough the channel. The TSCI may be a stream of wireless signals (e.g.CI).

The CI may be preprocessed, processed, postprocessed, stored (e.g. inlocal memory, portable/mobile memory, removable memory, storage network,cloud memory, in a volatile manner, in a non-volatile manner),retrieved, transmitted and/or received. One or more modem parametersand/or radio state parameters may be held constant. The modem parametersmay be applied to a radio subsystem. The modem parameters may representa radio state. A motion detection signal (e.g. baseband signal, and/orpacket decoded/demodulated from the baseband signal, etc.) may beobtained by processing (e.g. down-converting) the first wireless signal(e.g. RF/WiFi/LTE/5G signal) by the radio subsystem using the radiostate represented by the stored modem parameters. The modemparameters/radio state may be updated (e.g. using previous modemparameters or previous radio state). Both the previous and updated modemparameters/radio states may be applied in the radio subsystem in thedigital communication system. Both the previous and updated modemparameters/radio states may be compared/analyzed/processed/monitored inthe task.

The channel information may also be modem parameters (e.g. stored orfreshly computed) used to process the wireless signal. The wirelesssignal may comprise a plurality of probe signals. The same modemparameters may be used to process more than one probe signals. The samemodem parameters may also be used to process more than one wirelesssignals. The modem parameters may comprise parameters that indicatesettings or an overall configuration for the operation of a radiosubsystem or a baseband subsystem of a wireless sensor device (or both).The modem parameters may include one or more of: a gain setting, an RFfilter setting, an RF front end switch setting, a DC offset setting, oran IQ compensation setting for a radio subsystem, or a digital DCcorrection setting, a digital gain setting, and/or a digital filteringsetting (e.g. for a baseband subsystem). The CI may also be associatedwith information associated with a time period, time signature,timestamp, time amplitude, time phase, time trend, and/or timecharacteristics of the signal. The CI may be associated with informationassociated with a time-frequency partition, signature, amplitude, phase,trend, and/or characteristics of the signal. The CI may be associatedwith a decomposition of the signal. The CI may be associated withinformation associated with a direction, angle of arrival (AoA), angleof a directional antenna, and/or a phase of the signal through thechannel. The CI may be associated with attenuation patterns of thesignal through the channel. Each CI may be associated with a Type 1device and a Type 2 device. Each CI may be associated with an antenna ofthe Type 1 device and an antenna of the Type 2 device.

The CI may be obtained from a communication hardware (e.g. of Type 2device, or Type 1 device) that is capable of providing the CI. Thecommunication hardware may be a WiFi-capable chip/IC (integratedcircuit), chip compliant with a 802.11 or 802.16 or anotherwireless/radio standard, next generation WiFi-capable chip, LTE-capablechip, 5G-capable chip, 6G/7G/8G-capable chip, Bluetooth-enabled chip,NFC (near field communication)-enabled chip, BLE (Bluetooth lowpower)-enabled chip, UWB chip, another communication chip (e.g. Zigbee,WiMax, mesh network), etc. The communication hardware computes the CIand stores the CI in a buffer memory and make the CI available forextraction. The CI may comprise data and/or at least one matricesrelated to channel state information (CSI). The at least one matricesmay be used for channel equalization, and/or beam forming, etc. Thechannel may be associated with a venue. The attenuation may be due tosignal propagation in the venue, signalpropagating/reflection/refraction/diffraction through/at/around air(e.g. air of venue), refraction medium/reflection surface such as wall,doors, furniture, obstacles and/or barriers, etc. The attenuation may bedue to reflection at surfaces and obstacles (e.g. reflection surface,obstacle) such as floor, ceiling, furniture, fixtures, objects, people,pets, etc. Each CI may be associated with a timestamp. Each CI maycomprise N1 components (e.g. N1 frequency domain components in CFR, N1time domain components in CIR, or N1 decomposition components). Eachcomponent may be associated with a component index. Each component maybe a real, imaginary, or complex quantity, magnitude, phase, flag,and/or set. Each CI may comprise a vector or matrix of complex numbers,a set of mixed quantities, and/or a multi-dimensional collection of atleast one complex numbers.

Components of a TSCI associated with a particular component index mayform a respective component time series associated with the respectiveindex. A TSCI may be divided into N1 component time series. Eachrespective component time series is associated with a respectivecomponent index. The characteristics/STI of the motion of the object maybe monitored based on the component time series. In one example, one ormore ranges of CIC (e.g. one range being from component 11 to component23, a second range being from component 44 to component 50, and a thirdrange having only one component) may be selected based on somecriteria/cost function/signal quality metric (e.g. based onsignal-to-noise ratio, and/or interference level) for furtherprocessing.

A component-wise characteristic of a component-feature time series of aTSCI may be computed. The component-wise characteristics may be a scalar(e.g. energy) or a function with a domain and a range (e.g. anautocorrelation function, transform, inverse transform). Thecharacteristics/STI of the motion of the object may be monitored basedon the component-wise characteristics. A total characteristics (e.g.aggregate characteristics) of the TSCI may be computed based on thecomponent-wise characteristics of each component time series of theTSCI. The total characteristics may be a weighted average of thecomponent-wise characteristics. The characteristics/STI of the motion ofthe object may be monitored based on the total characteristics. Anaggregate quantity may be a weighted average of individual quantities.

The Type 1 device and Type 2 device may support WiFi, WiMax, 3G/beyond3G, 4G/beyond 4G, LTE, LTE-A, 5G, 6G, 7G, Bluetooth, NFC, BLE, Zigbee,UWB, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, meshnetwork, proprietary wireless system, IEEE 802.11 standard, 802.15standard, 802.16 standard, 3GPP standard, and/or another wirelesssystem.

A common wireless system and/or a common wireless channel may be sharedby the Type 1 transceiver and/or the at least one Type 2 transceiver.The at least one Type 2 transceiver may transmit respective signalcontemporaneously (or: asynchronously, synchronously, sporadically,continuously, repeatedly, concurrently, simultaneously and/ortemporarily) using the common wireless system and/or the common wirelesschannel. The Type 1 transceiver may transmit a signal to the at leastone Type 2 transceiver using the common wireless system and/or thecommon wireless channel.

Each Type 1 device and Type 2 device may have at least onetransmitting/receiving antenna. Each CI may be associated with one ofthe transmitting antenna of the Type 1 device and one of the receivingantenna of the Type 2 device. Each pair of a transmitting antenna and areceiving antenna may be associated with a link, a path, a communicationpath, signal hardware path, etc. For example, if the Type 1 device has M(e.g. 3) transmitting antennas, and the Type 2 device has N (e.g. 2)receiving antennas, there may be M×N (e.g. 3×2=6) links or paths. Eachlink or path may be associated with a TSCI.

The at least one TSCI may correspond to various antenna pairs betweenthe Type 1 device and the Type 2 device. The Type 1 device may have atleast one antenna. The Type 2 device may also have at least one antenna.Each TSCI may be associated with an antenna of the Type 1 device and anantenna of the Type 2 device. Averaging or weighted averaging overantenna links may be performed. The averaging or weighted averaging maybe over the at least one TSCI. The averaging may optionally be performedon a subset of the at least one TSCI corresponding to a subset of theantenna pairs.

Timestamps of CI of a portion of a TSCI may be irregular and may becorrected so that corrected timestamps of time-corrected CI may beuniformly spaced in time. In the case of multiple Type 1 devices and/ormultiple Type 2 devices, the corrected timestamp may be with respect tothe same or different clock. An original timestamp associated with eachof the CI may be determined. The original timestamp may not be uniformlyspaced in time. Original timestamps of all CI of the particular portionof the particular TSCI in the current sliding time window may becorrected so that corrected timestamps of time-corrected CI may beuniformly spaced in time.

The characteristics and/or STI (e.g. motion information) may comprise:location, location coordinate, change in location, position (e.g.initial position, new position), position on map, height, horizontallocation, vertical location, distance, displacement, speed,acceleration, rotational speed, rotational acceleration, direction,angle of motion, azimuth, direction of motion, rotation, path,deformation, transformation, shrinking, expanding, gait, gait cycle,head motion, repeated motion, periodic motion, pseudo-periodic motion,impulsive motion, sudden motion, fall-down motion, transient motion,behavior, transient behavior, period of motion, frequency of motion,time trend, temporal profile, temporal characteristics, occurrence,change, temporal change, change of CI, change in frequency, change intiming, change of gait cycle, timing, starting time, initiating time,ending time, duration, history of motion, motion type, motionclassification, frequency, frequency spectrum, frequencycharacteristics, presence, absence, proximity, approaching, receding,identity/identifier of the object, composition of the object, headmotion rate, head motion direction, mouth-related rate, eye-relatedrate, breathing rate, heart rate, tidal volume, depth of breath, inhaletime, exhale time, inhale time to exhale time ratio, airflow rate, heartheat-to-beat interval, heart rate variability, hand motion rate, handmotion direction, leg motion, body motion, walking rate, hand motionrate, positional characteristics, characteristics associated withmovement (e.g. change in position/location) of the object, tool motion,machine motion, complex motion, and/or combination of multiple motions,event, signal statistics, signal dynamics, anomaly, motion statistics,motion parameter, indication of motion detection, motion magnitude,motion phase, similarity score, distance score, Euclidean distance,weighted distance, L_1 norm, L_2 norm, L_k norm for k>2, statisticaldistance, correlation, correlation indicator, auto-correlation,covariance, auto-covariance, cross-covariance, inner product, outerproduct, motion signal transformation, motion feature, presence ofmotion, absence of motion, motion localization, motion identification,motion recognition, presence of object, absence of object, entrance ofobject, exit of object, a change of object, motion cycle, motion count,gait cycle, motion rhythm, deformation motion, gesture, handwriting,head motion, mouth motion, heart motion, internal organ motion, motiontrend, size, length, area, volume, capacity, shape, form, tag,starting/initiating location, ending location, starting/initiatingquantity, ending quantity, event, fall-down event, security event,accident event, home event, office event, factory event, warehouseevent, manufacturing event, assembly line event, maintenance event,car-related event, navigation event, tracking event, door event,door-open event, door-close event, window event, window-open event,window-close event, repeatable event, one-time event, consumed quantity,unconsumed quantity, state, physical state, health state, well-beingstate, emotional state, mental state, another event, analytics, outputresponses, and/or another information. The characteristics and/or STImay be computed/monitored based on a feature computed from a CI or aTSCI (e.g. feature computation/extraction). A static segment or profile(and/or a dynamic segment/profile) may beidentified/computed/analyzed/monitored/extracted/obtained/marked/disclosed/indicated/highlighted/stored/communicatedbased on an analysis of the feature. The analysis may comprise a motiondetection/movement assessment/presence detection. Computational workloadmay be shared among the Type 1 device, the Type 2 device and anotherprocessor.

The Type 1 device and/or Type 2 device may be a local device. The localdevice may be: a smart phone, smart device, TV, sound bar, set-top box,access point, router, repeater, wireless signal repeater/extender,remote control, speaker, fan, refrigerator, microwave, oven, coffeemachine, hot water pot, utensil, table, chair, light, lamp, door lock,camera, microphone, motion sensor, security device, fire hydrant, garagedoor, switch, power adapter, computer, dongle, computer peripheral,electronic pad, sofa, tile, accessory, home device, vehicle device,office device, building device, manufacturing device, watch, glasses,clock, television, oven, air-conditioner, accessory, utility, appliance,smart machine, smart vehicle, internet-of-thing (IoT) device,internet-enabled device, computer, portable computer, tablet, smarthouse, smart office, smart building, smart parking lot, smart system,and/or another device.

Each Type 1 device may be associated with a respective identifier (e.g.ID). Each Type 2 device may also be associated with a respectiveidentify (ID). The ID may comprise: numeral, combination of text andnumbers, name, password, account, account ID, web link, web address,index to some information, and/or another ID. The ID may be assigned.The ID may be assigned by hardware (e.g. hardwired, via dongle and/orother hardware), software and/or firmware. The ID may be stored (e.g. indatabase, in memory, in server (e.g. hub device), in the cloud, storedlocally, stored remotely, stored permanently, stored temporarily) andmay be retrieved. The ID may be associated with at least one record,account, user, household, address, phone number, social security number,customer number, another ID, another identifier, timestamp, and/orcollection of data. The ID and/or part of the ID of a Type 1 device maybe made available to a Type 2 device. The ID may be used forregistration, initialization, communication, identification,verification, detection, recognition, authentication, access control,cloud access, networking, social networking, logging, recording,cataloging, classification, tagging, association, pairing, transaction,electronic transaction, and/or intellectual property control, by theType 1 device and/or the Type 2 device.

The object may be person, user, subject, passenger, child, older person,baby, sleeping baby, baby in vehicle, patient, worker, high-valueworker, expert, specialist, waiter, customer in mall, traveler inairport/train station/bus terminal/shipping terminals,staff/worker/customer service personnel infactory/mall/supermarket/office/workplace, serviceman in sewage/airventilation system/lift well, lifts in lift wells, elevator, inmate,people to be tracked/monitored, animal, plant, living object, pet, dog,cat, smart phone, phone accessory, computer, tablet, portable computer,dongle, computing accessory, networked devices, WiFi devices, IoTdevices, smart watch, smart glasses, smart devices, speaker, keys, smartkey, wallet, purse, handbag, backpack, goods, cargo, luggage, equipment,motor, machine, air conditioner, fan, air conditioning equipment, lightfixture, moveable light, television, camera, audio and/or videoequipment, stationary, surveillance equipment, parts, signage, tool,cart, ticket, parking ticket, toll ticket, airplane ticket, credit card,plastic card, access card, food packaging, utensil, table, chair,cleaning equipment/tool, vehicle, car, cars in parking facilities,merchandise in warehouse/store/supermarket/distribution center, boat,bicycle, airplane, drone, remote control car/plane/boat, robot,manufacturing device, assembly line, material/unfinishedpart/robot/wagon/transports on factory floor, object to be tracked inairport/shopping mart/supermarket, non-object, absence of an object,presence of an object, object with form, object with changing form,object with no form, mass of fluid, mass of liquid, mass of gas/smoke,fire, flame, electromagnetic (EM) source, EM medium, and/or anotherobject. The object itself may be communicatively coupled with somenetwork, such as WiFi, MiFi, 3G/4G/LTE/5G/6G/7G, Bluetooth, NFC, BLE,WiMax, Zigbee, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA,mesh network, adhoc network, and/or other network. The object itself maybe bulky with AC power supply, but is moved during installation,cleaning, maintenance, renovation, etc. It may also be installed inmoveable platform such as lift, pad, movable, platform, elevator,conveyor belt, robot, drone, forklift, car, boat, vehicle, etc. Theobject may have multiple parts, each part with different movement (e.g.change in position/location). For example, the object may be a personwalking forward. While walking, his left hand and right hand may move indifferent direction, with different instantaneous speed, acceleration,motion, etc.

The wireless transmitter (e.g. Type 1 device), the wireless receiver(e.g. Type 2 device), another wireless transmitter and/or anotherwireless receiver may move with the object and/or another object (e.g.in prior movement, current movement and/or future movement. They may becommunicatively coupled to one or more nearby device. They may transmitTSCI and/or information associated with the TSCI to the nearby device,and/or each other. They may be with the nearby device. The wirelesstransmitter and/or the wireless receiver may be part of a small (e.g.coin-size, cigarette box size, or even smaller), light-weight portabledevice. The portable device may be wirelessly coupled with a nearbydevice.

The nearby device may be smart phone, iPhone, Android phone, smartdevice, smart appliance, smart vehicle, smart gadget, smart TV, smartrefrigerator, smart speaker, smart watch, smart glasses, smart pad,iPad, computer, wearable computer, notebook computer, gateway. Thenearby device may be connected to a cloud server, local server (e.g. hubdevice) and/or other server via internet, wired internet connectionand/or wireless internet connection. The nearby device may be portable.The portable device, the nearby device, a local server (e.g. hub device)and/or a cloud server may share the computation and/or storage for atask (e.g. obtain TSCI, determine characteristics/STI of the objectassociated with the movement (e.g. change in position/location) of theobject, computation of time series of power (e.g. signal strength)information, determining/computing the particular function, searchingfor local extremum, classification, identifying particular value of timeoffset, de-noising, processing, simplification, cleaning, wireless smartsensing task, extract CI from signal, switching, segmentation, estimatetrajectory/path/track, process the map, processing trajectory/path/trackbased on environment models/constraints/limitations, correction,corrective adjustment, adjustment, map-based (or model-based)correction, detecting error, checking for boundary hitting,thresholding) and information (e.g. TSCI). The nearby device may/may notmove with the object. The nearby device may be portable/notportable/moveable/non-moveable. The nearby device may use battery power,solar power, AC power and/or other power source. The nearby device mayhave replaceable/non-replaceable battery, and/orrechargeable/non-rechargeable battery. The nearby device may be similarto the object. The nearby device may have identical (and/or similar)hardware and/or software to the object. The nearby device may be a smartdevice, network enabled device, device with connection toWiFi/3G/4G/5G/6G/Zigbee/Bluetooth/NFC/UMTS/3GPP/GSM/EDGE/TDMA/FDMA/CDMA/WCDMA/TD-SCDMA/adhocnetwork/other network, smart speaker, smart watch, smart clock, smartappliance, smart machine, smart equipment, smart tool, smart vehicle,internet-of-thing (IoT) device, internet-enabled device, computer,portable computer, tablet, and another device. The nearby device and/orat least one processor associated with the wireless receiver, thewireless transmitter, the another wireless receiver, the anotherwireless transmitter and/or a cloud server (in the cloud) may determinethe initial STI of the object. Two or more of them may determine theinitial spatial-temporal info jointly. Two or more of them may shareintermediate information in the determination of the initial STI (e.g.initial position).

In one example, the wireless transmitter (e.g. Type 1 device, or TrackerBot) may move with the object. The wireless transmitter may send thesignal to the wireless receiver (e.g. Type 2 device, or Origin Register)or determining the initial STI (e.g. initial position) of the object.The wireless transmitter may also send the signal and/or another signalto another wireless receiver (e.g. another Type 2 device, or anotherOrigin Register) for the monitoring of the motion (spatial-temporalinfo) of the object. The wireless receiver may also receive the signaland/or another signal from the wireless transmitter and/or the anotherwireless transmitter for monitoring the motion of the object. Thelocation of the wireless receiver and/or the another wireless receivermay be known. In another example, the wireless receiver (e.g. Type 2device, or Tracker Bot) may move with the object. The wireless receivermay receive the signal transmitted from the wireless transmitter (e.g.Type 1 device, or Origin Register) for determining the initialspatial-temporal info (e.g. initial position) of the object. Thewireless receiver may also receive the signal and/or another signal fromanother wireless transmitter (e.g. another Type 1 device, or anotherOrigin Register) for the monitoring of the current motion (e.g.spatial-temporal info) of the object. The wireless transmitter may alsotransmit the signal and/or another signal to the wireless receiverand/or the another wireless receiver (e.g. another Type 2 device, oranother Tracker Bot) for monitoring the motion of the object. Thelocation of the wireless transmitter and/or the another wirelesstransmitter may be known.

The venue may be a space such as a sensing area, room, house, office,property, workplace, hallway, walkway, lift, lift well, escalator,elevator, sewage system, air ventilations system, staircase, gatheringarea, duct, air duct, pipe, tube, enclosed space, enclosed structure,semi-enclosed structure, enclosed area, area with at least one wall,plant, machine, engine, structure with wood, structure with glass,structure with metal, structure with walls, structure with doors,structure with gaps, structure with reflection surface, structure withfluid, building, rooftop, store, factory, assembly line, hotel room,museum, classroom, school, university, government building, warehouse,garage, mall, airport, train station, bus terminal, hub, transportationhub, shipping terminal, government facility, public facility, school,university, entertainment facility, recreational facility, hospital,pediatric/neonatal wards, seniors home, elderly care facility, geriatricfacility, community center, stadium, playground, park, field, sportsfacility, swimming facility, track and/or field, basketball court,tennis court, soccer stadium, baseball stadium, gymnasium, hall, garage,shopping mart, mall, supermarket, manufacturing facility, parkingfacility, construction site, mining facility, transportation facility,highway, road, valley, forest, wood, terrain, landscape, den, patio,land, path, amusement park, urban area, rural area, suburban area,metropolitan area, garden, square, plaza, music hall, downtown facility,over-air facility, semi-open facility, closed area, train platform,train station, distribution center, warehouse, store, distributioncenter, storage facility, underground facility, space (e.g. aboveground, outer-space) facility, floating facility, cavern, tunnelfacility, indoor facility, open-air facility, outdoor facility with somewalls/doors/reflective barriers, open facility, semi-open facility, car,truck, bus, van, container, ship/boat, submersible, train, tram,airplane, vehicle, mobile home, cave, tunnel, pipe, channel,metropolitan area, downtown area with relatively tall buildings, valley,well, duct, pathway, gas line, oil line, water pipe, network ofinterconnecting pathways/alleys/roads/tubes/cavities/caves/pipe-likestructure/air space/fluid space, human body, animal body, body cavity,organ, bone, teeth, soft tissue, hard tissue, rigid tissue, non-rigidtissue, blood/body fluid vessel, windpipe, air duct, den, etc. The venuemay be indoor space, outdoor space, The venue may include both theinside and outside of the space. For example, the venue may include boththe inside of a building and the outside of the building. For example,the venue can be a building that has one floor or multiple floors, and aportion of the building can be underground. The shape of the buildingcan be, e.g., round, square, rectangular, triangle, or irregular-shaped.These are merely examples. The disclosure can be used to detect eventsin other types of venue or spaces.

The wireless transmitter (e.g. Type 1 device) and/or the wirelessreceiver (e.g. Type 2 device) may be embedded in a portable device (e.g.a module, or a device with the module) that may move with the object(e.g. in prior movement and/or current movement). The portable devicemay be communicatively coupled with the object using a wired connection(e.g. through USB, microUSB, Firewire, HDMI, serial port, parallel port,and other connectors) and/or a connection (e.g. Bluetooth, Bluetooth LowEnergy (BLE), WiFi, LTE, NFC, ZigBee). The portable device may be alightweight device. The portable may be powered by battery, rechargeablebattery and/or AC power. The portable device may be very small (e.g. atsub-millimeter scale and/or sub-centimeter scale), and/or small (e.g.coin-size, card-size, pocket-size, or larger). The portable device maybe large, sizable, and/or bulky (e.g. heavy machinery to be installed).The portable device may be a WiFi hotspot, access point, mobile WiFi(MiFi), dongle with USB/micro USB/Firewire/other connector, smartphone,portable computer, computer, tablet, smart device, internet-of-thing(IoT) device, WiFi-enabled device, LTE-enabled device, a smart watch,smart glass, smart mirror, smart antenna, smart battery, smart light,smart pen, smart ring, smart door, smart window, smart clock, smallbattery, smart wallet, smart belt, smart handbag, smartclothing/garment, smart ornament, smart packaging, smartpaper/book/magazine/poster/printed matter/signage/display/lightedsystem/lighting system, smart key/tool, smartbracelet/chain/necklace/wearable/accessory, smart pad/cushion, smarttile/block/brick/building material/other material, smart garbagecan/waste container, smart food carriage/storage, smart ball/racket,smart chair/sofa/bed, smart shoe/footwear/carpet/mat/shoe rack, smartglove/hand wear/ring/hand ware, smarthat/headwear/makeup/sticker/tattoo, smart mirror, smart toy, smart pill,smart utensil, smart bottle/food container, smart tool, smart device,IoT device, WiFi enabled device, network enabled device, 3G/4G/5G/6Genabled device, UMTS devices, 3GPP devices, GSM devices, EDGE devices,TDMA devices, FDMA devices, CDMA devices, WCDMA devices, TD-SCDMAdevices, embeddable device, implantable device, air conditioner,refrigerator, heater, furnace, furniture, oven, cooking device,television/set-top box (STB)/DVD player/audio player/video player/remotecontrol, hi-fi, audio device, speaker, lamp/light, wall, door, window,roof, roof tile/shingle/structure/atticstructure/device/feature/installation/fixtures, lawn mower/gardentools/yard tools/mechanics tools/garage tools/, garbage can/container,20-ft/40-ft container, storage container,factory/manufacturing/production device, repair tools, fluid container,machine, machinery to be installed, vehicle, cart, wagon, warehousevehicle, car, bicycle, motorcycle, boat, vessel, airplane,basket/box/bag/bucket/container, smartplate/cup/bowl/pot/mat/utensils/kitchen tools/kitchen devices/kitchenaccessories/cabinets/tables/chairs/tiles/lights/water pipes/taps/gasrange/oven/dishwashing machine/etc. The portable device may have abattery that may be replaceable, irreplaceable, rechargeable, and/ornon-rechargeable. The portable device may be wirelessly charged. Theportable device may be a smart payment card. The portable device may bea payment card used in parking lots, highways, entertainment parks, orother venues/facilities that need payment. The portable device may havean identity (ID)/identifier as described above.

An event may be monitored based on the TSCI. The event may be an objectrelated event, such as fall-down of the object (e.g. an person and/or asick person), rotation, hesitation, pause, impact (e.g. a person hittinga sandbag, door, window, bed, chair, table, desk, cabinet, box, anotherperson, animal, bird, fly, table, chair, ball, bowling ball, tennisball, football, soccer ball, baseball, basketball, volley ball),two-body action (e.g. a person letting go a balloon, catching a fish,molding a clay, writing a paper, person typing on a computer), carmoving in a garage, person carrying a smart phone and walking around anairport/mall/government building/office/etc., autonomous moveableobject/machine moving around (e.g. vacuum cleaner, utility vehicle, car,drone, self-driving car). The task or the wireless smart sensing taskmay comprise: object detection, presence detection, proximity detection,object recognition, activity recognition, object verification, objectcounting, daily activity monitoring, well-being monitoring, vital signmonitoring, health condition monitoring, baby monitoring, elderlymonitoring, sleep monitoring, sleep stage monitoring, walkingmonitoring, exercise monitoring, tool detection, tool recognition, toolverification, patient detection, patient monitoring, patientverification, machine detection, machine recognition, machineverification, human detection, human recognition, human verification,baby detection, baby recognition, baby verification, human breathingdetection, human breathing recognition, human breathing estimation,human breathing verification, human heart beat detection, human heartbeat recognition, human heart beat estimation, human heart beatverification, fall-down detection, fall-down recognition, fall-downestimation, fall-down verification, emotion detection, emotionrecognition, emotion estimation, emotion verification, motion detection,motion degree estimation, motion recognition, motion estimation, motionverification, periodic motion detection, periodic motion recognition,periodic motion estimation, periodic motion verification, repeatedmotion detection, repeated motion recognition, repeated motionestimation, repeated motion verification, stationary motion detection,stationary motion recognition, stationary motion estimation, stationarymotion verification, cyclo-stationary motion detection, cyclo-stationarymotion recognition, cyclo-stationary motion estimation, cyclo-stationarymotion verification, transient motion detection, transient motionrecognition, transient motion estimation, transient motion verification,trend detection, trend recognition, trend estimation, trendverification, breathing detection, breathing recognition, breathingestimation, breathing estimation, human biometrics detection, humanbiometric recognition, human biometrics estimation, human biometricsverification, environment informatics detection, environment informaticsrecognition, environment informatics estimation, environment informaticsverification, gait detection, gait recognition, gait estimation, gaitverification, gesture detection, gesture recognition, gestureestimation, gesture verification, machine learning, supervised learning,unsupervised learning, semi-supervised learning, clustering, featureextraction, featuring training, principal component analysis,eigen-decomposition, frequency decomposition, time decomposition,time-frequency decomposition, functional decomposition, otherdecomposition, training, discriminative training, supervised training,unsupervised training, semi-supervised training, neural network, suddenmotion detection, fall-down detection, danger detection, life-threatdetection, regular motion detection, stationary motion detection,cyclo-stationary motion detection, intrusion detection, suspiciousmotion detection, security, safety monitoring, navigation, guidance,map-based processing, map-based correction, model-basedprocessing/correction, irregularity detection, locationing, roomsensing, tracking, multiple object tracking, indoor tracking, indoorposition, indoor navigation, energy management, power transfer, wirelesspower transfer, object counting, car tracking in parking garage,activating a device/system (e.g. security system, access system, alarm,siren, speaker, television, entertaining system, camera,heater/air-conditioning (HVAC) system, ventilation system, lightingsystem, gaming system, coffee machine, cooking device, cleaning device,housekeeping device), geometry estimation, augmented reality, wirelesscommunication, data communication, signal broadcasting, networking,coordination, administration, encryption, protection, cloud computing,other processing and/or other task. The task may be performed by theType 1 device, the Type 2 device, another Type 1 device, another Type 2device, a nearby device, a local server (e.g. hub device), edge server,a cloud server, and/or another device. The task may be based on TSCIbetween any pair of Type 1 device and Type 2 device. A Type 2 device maybe a Type 1 device, and vice versa. A Type 2 device may play/perform therole (e.g. functionality) of Type 1 device temporarily, continuously,sporadically, simultaneously, and/or contemporaneously, and vice versa.A first part of the task may comprise at least one of: preprocessing,processing, signal conditioning, signal processing, post-processing,processingsporadically/continuously/simultaneously/contemporaneously/dynamically/adaptivelon-demand/as-needed, calibrating, denoising, feature extraction, coding,encryption, transformation, mapping, motion detection, motionestimation, motion change detection, motion pattern detection, motionpattern estimation, motion pattern recognition, vital sign detection,vital sign estimation, vital sign recognition, periodic motiondetection, periodic motion estimation, repeated motiondetection/estimation, breathing rate detection, breathing rateestimation, breathing pattern detection, breathing pattern estimation,breathing pattern recognition, heart beat detection, heart beatestimation, heart pattern detection, heart pattern estimation, heartpattern recognition, gesture detection, gesture estimation, gesturerecognition, speed detection, speed estimation, object locationing,object tracking, navigation, acceleration estimation, accelerationdetection, fall-down detection, change detection, intruder (and/orillegal action) detection, baby detection, baby monitoring, patientmonitoring, object recognition, wireless power transfer, and/or wirelesscharging.

A second part of the task may comprise at least one of: a smart hometask, smart office task, smart building task, smart factory task (e.g.manufacturing using a machine or an assembly line), smartinternet-of-thing (IoT) task, smart system task, smart home operation,smart office operation, smart building operation, smart manufacturingoperation (e.g. moving supplies/parts/raw material to a machine/anassembly line), IoT operation, smart system operation, turning on alight, turning off the light, controlling the light in at least one of:a room, region, and/or the venue, playing a sound clip, playing thesound clip in at least one of: the room, the region, and/or the venue,playing the sound clip of at least one of: a welcome, greeting,farewell, first message, and/or a second message associated with thefirst part of the task, turning on an appliance, turning off theappliance, controlling the appliance in at least one of: the room, theregion, and/or the venue, turning on an electrical system, turning offthe electrical system, controlling the electrical system in at least oneof: the room, the region, and/or the venue, turning on a securitysystem, turning off the security system, controlling the security systemin at least one of: the room, the region, and/or the venue, turning on amechanical system, turning off a mechanical system, controlling themechanical system in at least one of: the room, the region, and/or thevenue, and/or controlling at least one of: an air conditioning system,heating system, ventilation system, lighting system, heating device,stove, entertainment system, door, fence, window, garage, computersystem, networked device, networked system, home appliance, officeequipment, lighting device, robot (e.g. robotic arm), smart vehicle,smart machine, assembly line, smart device, internet-of-thing (IoT)device, smart home device, and/or a smart office device.

The task may include: detect a user returning home, detect a userleaving home, detect a user moving from one room to another,detect/control/lock/unlock/open/close/partially open awindow/door/garage door/blind/curtain/panel/solar panel/sun shade,detect a pet, detect/monitor a user doing something (e.g. sleeping onsofa, sleeping in bedroom, running on treadmill, cooking, sitting onsofa, watching TV, eating in kitchen, eating in dining room, goingupstairs/downstairs, going outside/coming back, in the rest room),monitor/detect location of a user/pet, do something (e.g. send amessage, notify/report to someone) automatically upon detection, dosomething for the user automatically upon detecting the user, turnon/off/dim a light, turn on/off music/radio/home entertainment system,turn on/off/adjust/control TV/HiFi/set-top-box (STB)/home entertainmentsystem/smart speaker/smart device, turn on/off/adjust air conditioningsystem, turn on/off/adjust ventilation system, turn on/off/adjustheating system, adjust/control curtains/light shades, turn on/off/wake acomputer, turn on/off/pre-heat/control coffee machine/hot water pot,turn on/off/control/preheat cooker/oven/microwave oven/another cookingdevice, check/adjust temperature, check weather forecast, checktelephone message box, check mail, do a system check, control/adjust asystem, check/control/arm/disarm security system/baby monitor,check/control refrigerator, give a report (e.g. through a speaker suchas Google home, Amazon Echo, on a display/screen, via awebpage/email/messaging system/notification system).

For example, when a user arrives home in his car, the task may be to,automatically, detect the user or his car approaching, open the garagedoor upon detection, turn on the driveway/garage light as the userapproaches the garage, turn on air conditioner/heater/fan, etc. As theuser enters the house, the task may be to, automatically, turn on theentrance light, turn off driveway/garage light, play a greeting messageto welcome the user, turn on the music, turn on the radio and tuning tothe user's favorite radio news channel, open the curtain/blind, monitorthe user's mood, adjust the lighting and sound environment according tothe user's mood or the current/imminent event (e.g. do romantic lightingand music because the user is scheduled to eat dinner with girlfriend in1 hour) on the user's daily calendar, warm the food in microwave thatthe user prepared in the morning, do a diagnostic check of all systemsin the house, check weather forecast for tomorrow's work, check news ofinterest to the user, check user's calendar and to-do list and playreminder, check telephone answer system/messaging system/email and givea verbal report using dialog system/speech synthesis, remind (e.g. usingaudible tool such as speakers/HiFi/speechsynthesis/sound/voice/music/song/sound field/background soundfield/dialog system, using visual tool such as TV/entertainmentsystem/computer/notebook/smartpad/display/light/color/brightness/patterns/symbols, using haptictool/virtual reality tool/gesture/tool, using a smartdevice/appliance/material/furniture/fixture, using web tool/server/hubdevice/cloud server/fog server/edge server/home network/mesh network,using messaging tool/notification tool/communication tool/schedulingtool/email, using user interface/GUI, using scent/smell/fragrance/taste,using neural tool/nervous system tool, using a combination) the user ofhis mother's birthday and to call her, prepare a report, and give thereport (e.g. using a tool for reminding as discussed above). The taskmay turn on the air conditioner/heater/ventilation system in advance, oradjust temperature setting of smart thermostat in advance, etc. As theuser moves from the entrance to the living room, the task may be to turnon the living room light, open the living room curtain, open the window,turn off the entrance light behind the user, turn on the TV and set-topbox, set TV to the user's favorite channel, adjust an applianceaccording to the user's preference and conditions/states (e.g. adjustlighting and choose/play music to build a romantic atmosphere), etc.

Another example may be: When the user wakes up in the morning, the taskmay be to detect the user moving around in the bedroom, open theblind/curtain, open the window, turn off the alarm clock, adjust indoortemperature from night-time temperature profile to day-time temperatureprofile, turn on the bedroom light, turn on the restroom light as theuser approaches the restroom, check radio or streaming channel and playmorning news, turn on the coffee machine and preheat the water, turn offsecurity system, etc. When the user walks from bedroom to kitchen, thetask may be to turn on the kitchen and hallway lights, turn off thebedroom and restroom lights, move the music/message/reminder from thebedroom to the kitchen, turn on the kitchen TV, change TV to morningnews channel, lower the kitchen blind and open the kitchen window tobring in fresh air, unlock backdoor for the user to check the backyard,adjust temperature setting for the kitchen, etc. Another example may be:When the user leaves home for work, the task may be to detect the userleaving, play a farewell and/or have-a-good-day message, open/closegarage door, turn on/off garage light and driveway light, turn off/dimlights to save energy (just in case the user forgets), close/lock allwindows/doors (just in case the user forgets), turn off appliance(especially stove, oven, microwave oven), turn on/arm the home securitysystem to guard the home against any intruder, adjust airconditioning/heating/ventilation systems to “away-from-home” profile tosave energy, send alerts/reports/updates to the user's smart phone, etc.

A motion may comprise at least one of: a no-motion, resting motion,non-moving motion, movement, change in position/location, deterministicmotion, transient motion, fall-down motion, repeating motion, periodicmotion, pseudo-periodic motion, periodic/repeated motion associated withbreathing, periodic/repeated motion associated with heartbeat,periodic/repeated motion associated with living object,periodic/repeated motion associated with machine, periodic/repeatedmotion associated with man-made object, periodic/repeated motionassociated with nature, complex motion with transient element andperiodic element, repetitive motion, non-deterministic motion,probabilistic motion, chaotic motion, random motion, complex motion withnon-deterministic element and deterministic element, stationary randommotion, pseudo-stationary random motion, cyclo-stationary random motion,non-stationary random motion, stationary random motion with periodicautocorrelation function (ACF), random motion with periodic ACF forperiod of time, random motion that is pseudo-stationary for a period oftime, random motion of which an instantaneous ACF has apseudo-periodic/repeating element for a period of time, machine motion,mechanical motion, vehicle motion, drone motion, air-related motion,wind-related motion, weather-related motion, water-related motion,fluid-related motion, ground-related motion, change in electro-magneticcharacteristics, sub-surface motion, seismic motion, plant motion,animal motion, human motion, normal motion, abnormal motion, dangerousmotion, warning motion, suspicious motion, rain, fire, flood, tsunami,explosion, collision, imminent collision, human body motion, headmotion, facial motion, eye motion, mouth motion, tongue motion, neckmotion, finger motion, hand motion, arm motion, shoulder motion, bodymotion, chest motion, abdominal motion, hip motion, leg motion, footmotion, body joint motion, knee motion, elbow motion, upper body motion,lower body motion, skin motion, below-skin motion, subcutaneous tissuemotion, blood vessel motion, intravenous motion, organ motion, heartmotion, lung motion, stomach motion, intestine motion, bowel motion,eating motion, breathing motion, facial expression, eye expression,mouth expression, talking motion, singing motion, eating motion,gesture, hand gesture, arm gesture, keystroke, typing stroke,user-interface gesture, man-machine interaction, gait, dancing movement,coordinated movement, and/or coordinated body movement.

The heterogeneous IC of the Type 1 device and/or any Type 2 receiver maycomprise low-noise amplifier (LNA), power amplifier, transmit-receiveswitch, media access controller, baseband radio, 2.4 GHz radio, 3.65 GHzradio, 4.9 GHz radio, 5 GHz radio, 5.9 GHz radio, below 6 GHz radio,below 60 GHz radio and/or another radio. The heterogeneous IC maycomprise a processor, a memory communicatively coupled with theprocessor, and a set of instructions stored in the memory to be executedby the processor. The IC and/or any processor may comprise at least oneof: general purpose processor, special purpose processor,microprocessor, multi-processor, multi-core processor, parallelprocessor, CISC processor, RISC processor, microcontroller, centralprocessing unit (CPU), graphical processor unit (GPU), digital signalprocessor (DSP), application specific integrated circuit (ASIC), fieldprogrammable gate array (FPGA), embedded processor (e.g. ARM), logiccircuit, other programmable logic device, discrete logic, and/or acombination. The heterogeneous IC may support broadband network,wireless network, mobile network, mesh network, cellular network,wireless local area network (WLAN), wide area network (WAN), andmetropolitan area network (MAN), WLAN standard, WiFi, LTE, LTE-A, LTE-U,802.11 standard, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad,802.11af, 802.11ah, 802.11ax, 802.11ay, mesh network standard, 802.15standard, 802.16 standard, cellular network standard, 3G, 3.5G, 4G,beyond 4G, 4.5G, 5G, 6G, 7G, 8G, 9G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA,CDMA, WCDMA, TD-SCDMA, Bluetooth, Bluetooth Low-Energy (BLE), NFC,Zigbee, WiMax, and/or another wireless network protocol.

The processor may comprise general purpose processor, special purposeprocessor, microprocessor, microcontroller, embedded processor, digitalsignal processor, central processing unit (CPU), graphical processingunit (GPU), multi-processor, multi-core processor, and/or processor withgraphics capability, and/or a combination. The memory may be volatile,non-volatile, random access memory (RAM), Read Only Memory (ROM),Electrically Programmable ROM (EPROM), Electrically ErasableProgrammable ROM (EEPROM), hard disk, flash memory, CD-ROM, DVD-ROM,magnetic storage, optical storage, organic storage, storage system,storage network, network storage, cloud storage, edge storage, localstorage, external storage, internal storage, or other form ofnon-transitory storage medium known in the art. The set of instructions(machine executable code) corresponding to the method steps may beembodied directly in hardware, in software, in firmware, or incombinations thereof. The set of instructions may be embedded,pre-loaded, loaded upon boot up, loaded on the fly, loaded on demand,pre-installed, installed, and/or downloaded.

The presentation may be a presentation in an audio-visual way (e.g.using combination of visual, graphics, text, symbols, color, shades,video, animation, sound, speech, audio, etc.), graphical way (e.g. usingGUI, animation, video), textual way (e.g. webpage with text, message,animated text), symbolic way (e.g. emoticon, signs, hand gesture), ormechanical way (e.g. vibration, actuator movement, haptics, etc.).

Computational workload associated with the method is shared among theprocessor, the Type 1 heterogeneous wireless device, the Type 2heterogeneous wireless device, a local server (e.g. hub device), a cloudserver, and another processor.

An operation, pre-processing, processing and/or postprocessing may beapplied to data (e.g. TSCI, autocorrelation, features of TSCI). Anoperation may be preprocessing, processing and/or postprocessing. Thepreprocessing, processing and/or postprocessing may be an operation. Anoperation may comprise preprocessing, processing, post-processing,scaling, computing a confidence factor, computing a line-of-sight (LOS)quantity, computing a non-LOS (NLOS) quantity, a quantity comprising LOSand NLOS, computing a single link (e.g. path, communication path, linkbetween a transmitting antenna and a receiving antenna) quantity,computing a quantity comprising multiple links, computing a function ofthe operands, filtering, linear filtering, nonlinear filtering, folding,grouping, energy computation, lowpass filtering, bandpass filtering,highpass filtering, median filtering, rank filtering, quartilefiltering, percentile filtering, mode filtering, finite impulse response(FIR) filtering, infinite impulse response (IIR) filtering, movingaverage (MA) filtering, autoregressive (AR) filtering, autoregressivemoving averaging (ARMA) filtering, selective filtering, adaptivefiltering, interpolation, decimation, subsampling, upsampling,resampling, time correction, time base correction, phase correction,magnitude correction, phase cleaning, magnitude cleaning, matchedfiltering, enhancement, restoration, denoising, smoothing, signalconditioning, enhancement, restoration, spectral analysis, lineartransform, nonlinear transform, inverse transform, frequency transform,inverse frequency transform, Fourier transform (FT), discrete time FT(DTFT), discrete FT (DFT), fast FT (FFT), wavelet transform, Laplacetransform, Hilbert transform, Hadamard transform, trigonometrictransform, sine transform, cosine transform, DCT, power-of-2 transform,sparse transform, graph-based transform, graph signal processing, fasttransform, a transform combined with zero padding, cyclic padding,padding, zero padding, feature extraction, decomposition, projection,orthogonal projection, non-orthogonal projection, over-completeprojection, eigen-decomposition, singular value decomposition (SVD),principle component analysis (PCA), independent component analysis(ICA), grouping, sorting, thresholding, soft thresholding, hardthresholding, clipping, soft clipping, first derivative, second orderderivative, high order derivative, convolution, multiplication,division, addition, subtraction, integration, maximization,minimization, least mean square error, recursive least square,constrained least square, batch least square, least absolute error,least mean square deviation, least absolute deviation, localmaximization, local minimization, optimization of a cost function,neural network, recognition, labeling, training, clustering, machinelearning, supervised learning, unsupervised learning, semi-supervisedlearning, comparison with another TSCI, similarity score computation,quantization, vector quantization, matching pursuit, compression,encryption, coding, storing, transmitting, normalization, temporalnormalization, frequency domain normalization, classification,clustering, labeling, tagging, learning, detection, estimation, learningnetwork, mapping, remapping, expansion, storing, retrieving,transmitting, receiving, representing, merging, combining, splitting,tracking, monitoring, matched filtering, Kalman filtering, particlefilter, intrapolation, extrapolation, histogram estimation, importancesampling, Monte Carlo sampling, compressive sensing, representing,merging, combining, splitting, scrambling, error protection, forwarderror correction, doing nothing, time varying processing, conditioningaveraging, weighted averaging, arithmetic mean, geometric mean, harmonicmean, averaging over selected frequency, averaging over antenna links,logical operation, permutation, combination, sorting, AND, OR, XOR,union, intersection, vector addition, vector subtraction, vectormultiplication, vector division, inverse, norm, distance, and/or anotheroperation. The operation may be the preprocessing, processing, and/orpost-processing. Operations may be applied jointly on multiple timeseries or functions.

The function (e.g. function of operands) may comprise: scalar function,vector function, discrete function, continuous function, polynomialfunction, characteristics, feature, magnitude, phase, exponentialfunction, logarithmic function, trigonometric function, transcendentalfunction, logical function, linear function, algebraic function,nonlinear function, piecewise linear function, real function, complexfunction, vector-valued function, inverse function, derivative offunction, integration of function, circular function, function ofanother function, one-to-one function, one-to-many function, many-to-onefunction, many-to-many function, zero crossing, absolute function,indicator function, mean, mode, median, range, statistics, histogram,variance, standard deviation, measure of variation, spread, dispersion,deviation, divergence, range, interquartile range, total variation,absolute deviation, total deviation, arithmetic mean, geometric mean,harmonic mean, trimmed mean, percentile, square, cube, root, power,sine, cosine, tangent, cotangent, secant, cosecant, elliptical function,parabolic function, hyperbolic function, game function, zeta function,absolute value, thresholding, limiting function, floor function,rounding function, sign function, quantization, piecewise constantfunction, composite function, function of function, time functionprocessed with an operation (e.g. filtering), probabilistic function,stochastic function, random function, ergodic function, stationaryfunction, deterministic function, periodic function, repeated function,transformation, frequency transform, inverse frequency transform,discrete time transform, Laplace transform, Hilbert transform, sinetransform, cosine transform, triangular transform, wavelet transform,integer transform, power-of-2 transform, sparse transform, projection,decomposition, principle component analysis (PCA), independent componentanalysis (ICA), neural network, feature extraction, moving function,function of moving window of neighboring items of time series, filteringfunction, convolution, mean function, histogram, variance/standarddeviation function, statistical function, short-time transform, discretetransform, discrete Fourier transform, discrete cosine transform,discrete sine transform, Hadamard transform, eigen-decomposition,eigenvalue, singular value decomposition (SVD), singular value,orthogonal decomposition, matching pursuit, sparse transform, sparseapproximation, any decomposition, graph-based processing, graph-basedtransform, graph signal processing, classification, identifying aclass/group/category, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, zero crossing,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, down-converting, upsampling,up-converting, interpolation, extrapolation, importance sampling, MonteCarlo sampling, compressive sensing, statistics, short term statistics,long term statistics, autocorrelation function, cross correlation,moment generating function, time averaging, weighted averaging, specialfunction, Bessel function, error function, complementary error function,Beta function, Gamma function, integral function, Gaussian function,Poisson function, etc. Machine learning, training, discriminativetraining, deep learning, neural network, continuous time processing,distributed computing, distributed storage, acceleration usingGPU/DSP/coprocessor/multicore/multiprocessing may be applied to a step(or each step) of this disclosure.

A frequency transform may include Fourier transform, Laplace transform,Hadamard transform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, combined zero padding and transform, Fourier transform withzero padding, and/or another transform. Fast versions and/orapproximated versions of the transform may be performed. The transformmay be performed using floating point, and/or fixed point arithmetic.

An inverse frequency transform may include inverse Fourier transform,inverse Laplace transform, inverse Hadamard transform, inverse Hilberttransform, inverse sine transform, inverse cosine transform, inversetriangular transform, inverse wavelet transform, inverse integertransform, inverse power-of-2 transform, combined zero padding andtransform, inverse Fourier transform with zero padding, and/or anothertransform. Fast versions and/or approximated versions of the transformmay be performed. The transform may be performed using floating point,and/or fixed point arithmetic.

A quantity/feature from a TSCI may be computed. The quantity maycomprise statistic of at least one of: motion, location, map coordinate,height, speed, acceleration, movement angle, rotation, size, volume,time trend, pattern, one-time pattern, repeating pattern, evolvingpattern, time pattern, mutually excluding patterns, related/correlatedpatterns, cause-and-effect, correlation, short-term/long-termcorrelation, tendency, inclination, statistics, typical behavior,atypical behavior, time trend, time profile, periodic motion, repeatedmotion, repetition, tendency, change, abrupt change, gradual change,frequency, transient, breathing, gait, action, event, suspicious event,dangerous event, alarming event, warning, belief, proximity, collision,power, signal, signal power, signal strength, signal intensity, receivedsignal strength indicator (RSSI), signal amplitude, signal phase, signalfrequency component, signal frequency band component, channel stateinformation (CSI), map, time, frequency, time-frequency, decomposition,orthogonal decomposition, non-orthogonal decomposition, tracking,breathing, heart beat, statistical parameters, cardiopulmonarystatistics/analytics (e.g. output responses), daily activitystatistics/analytics, chronic disease statistics/analytics, medicalstatistics/analytics, an early (or instantaneous or contemporaneous ordelayed) indication/suggestion/sign/indicator/verifier/detection/symptomof a disease/condition/situation, biometric, baby, patient, machine,device, temperature, vehicle, parking lot, venue, lift, elevator,spatial, road, fluid flow, home, room, office, house, building,warehouse, storage, system, ventilation, fan, pipe, duct, people, human,car, boat, truck, airplane, drone, downtown, crowd, impulsive event,cyclo-stationary, environment, vibration, material, surface,3-dimensional, 2-dimensional, local, global, presence, and/or anothermeasurable quantity/variable.

Sliding time window may have time varying window width. It may besmaller at the beginning to enable fast acquisition and may increaseover time to a steady-state size. The steady-state size may be relatedto the frequency, repeated motion, transient motion, and/or STI to bemonitored. Even in steady state, the window size may be adaptively(and/or dynamically) changed (e.g. adjusted, varied, modified) based onbattery life, power consumption, available computing power, change inamount of targets, the nature of motion to be monitored, etc.

The time shift between two sliding time windows at adjacent timeinstance may be constant/variable/locally adaptive/dynamically adjustedover time. When shorter time shift is used, the update of any monitoringmay be more frequent which may be used for fast changing situations,object motions, and/or objects. Longer time shift may be used for slowersituations, object motions, and/or objects. The window width/size and/ortime shift may be changed (e.g. adjusted, varied, modified) upon a userrequest/choice. The time shift may be changed automatically (e.g. ascontrolled by processor/computer/server/hub device/cloud server) and/oradaptively (and/or dynamically).

At least one characteristics (e.g. characteristic value, orcharacteristic point) of a function (e.g. auto-correlation function,auto-covariance function, cross-correlation function, cross-covariancefunction, power spectral density, time function, frequency domainfunction, frequency transform) may be determined (e.g. by an objecttracking server, the processor, the Type 1 heterogeneous device, theType 2 heterogeneous device, and/or another device). The at least onecharacteristics of the function may include: a maximum, minimum,extremum, local maximum, local minimum, local extremum, local extremumwith positive time offset, first local extremum with positive timeoffset, n{circumflex over ( )}th local extremum with positive timeoffset, local extremum with negative time offset, first local extremumwith negative time offset, n{circumflex over ( )}th local extremum withnegative time offset, constrained maximum, constrained minimum,constrained extremum, significant maximum, significant minimum,significant extremum, slope, derivative, higher order derivative,maximum slope, minimum slope, local maximum slope, local maximum slopewith positive time offset, local minimum slope, constrained maximumslope, constrained minimum slope, maximum higher order derivative,minimum higher order derivative, constrained higher order derivative,zero-crossing, zero crossing with positive time offset, n{circumflexover ( )}th zero crossing with positive time offset, zero crossing withnegative time offset, n{circumflex over ( )}th zero crossing withnegative time offset, constrained zero-crossing, zero-crossing of slope,zero-crossing of higher order derivative, and/or anothercharacteristics. At least one argument of the function associated withthe at least one characteristics of the function may be identified. Somequantity (e.g. spatial-temporal information of the object) may bedetermined based on the at least one argument of the function.

A characteristics (e.g. characteristics of motion of an object in thevenue) may comprise at least one of: an instantaneous characteristics,short-term characteristics, repetitive characteristics, recurringcharacteristics, history, incremental characteristics, changingcharacteristics, deviational characteristics, phase, magnitude, degree,time characteristics, frequency characteristics, time-frequencycharacteristics, decomposition characteristics, orthogonal decompositioncharacteristics, non-orthogonal decomposition characteristics,deterministic characteristics, probabilistic characteristics, stochasticcharacteristics, autocorrelation function (ACF), mean, variance,standard deviation, measure of variation, spread, dispersion, deviation,divergence, range, interquartile range, total variation, absolutedeviation, total deviation, statistics, duration, timing, trend,periodic characteristics, repetition characteristics, long-termcharacteristics, historical characteristics, average characteristics,current characteristics, past characteristics, future characteristics,predicted characteristics, location, distance, height, speed, direction,velocity, acceleration, change of the acceleration, angle, angularspeed, angular velocity, angular acceleration of the object, change ofthe angular acceleration, orientation of the object, angular ofrotation, deformation of the object, shape of the object, change ofshape of the object, change of size of the object, change of structureof the object, and/or change of characteristics of the object.

At least one local maximum and at least one local minimum of thefunction may be identified. At least one localsignal-to-noise-ratio-like (SNR-like) parameter may be computed for eachpair of adjacent local maximum and local minimum. The SNR-like parametermay be a function (e.g. linear, log, exponential function, monotonicfunction) of a fraction of a quantity (e.g. power, magnitude) of thelocal maximum over the same quantity of the local minimum. It may alsobe the function of a difference between the quantity of the localmaximum and the same quantity of the local minimum. Significant localpeaks may be identified or selected. Each significant local peak may bea local maximum with SNR-like parameter greater than a threshold T1and/or a local maximum with amplitude greater than a threshold T2. Theat least one local minimum and the at least one local minimum in thefrequency domain may be identified/computed using a persistence-basedapproach.

A set of selected significant local peaks may be selected from the setof identified significant local peaks based on a selection criterion(e.g. a quality criterion, a signal quality condition). Thecharacteristics/STI of the object may be computed based on the set ofselected significant local peaks and frequency values associated withthe set of selected significant local peaks. In one example, theselection criterion may always correspond to select the strongest peaksin a range. While the strongest peaks may be selected, the unselectedpeaks may still be significant (rather strong).

Unselected significant peaks may be stored and/or monitored as“reserved” peaks for use in future selection in future sliding timewindows. As an example, there may be a particular peak (at a particularfrequency) appearing consistently over time. Initially, it may besignificant but not selected (as other peaks may be stronger). But inlater time, the peak may become stronger and more dominant and may beselected. When it became “selected”, it may be back-traced in time andmade “selected” in the earlier time when it was significant but notselected. In such case, the back-traced peak may replace a previouslyselected peak in an early time. The replaced peak may be the relativelyweakest, or a peak that appear in isolation in time (i.e. appearing onlybriefly in time).

In another example, the selection criterion may not correspond to selectthe strongest peaks in the range. Instead, it may consider not only the“strength” of the peak, but the “trace” of the peak—peaks that may havehappened in the past, especially those peaks that have been identifiedfor a long time. For example, if a finite state machine (FSM) is used,it may select the peak(s) based on the state of the FSM. Decisionthresholds may be computed adaptively (and/or dynamically) based on thestate of the FSM.

A similarity score and/or component similarity score may be computed(e.g. by a server (e.g. hub device), the processor, the Type 1 device,the Type 2 device, a local server, a cloud server, and/or anotherdevice) based on a pair of temporally adjacent CI of a TSCI. The pairmay come from the same sliding window or two different sliding windows.The similarity score may also be based on a pair of, temporally adjacentor not so adjacent, CI from two different TSCI. The similarity scoreand/or component similar score may be/comprise: time reversal resonatingstrength (TRRS), correlation, cross-correlation, auto-correlation,correlation indicator, covariance, cross-covariance, auto-covariance,inner product of two vectors, distance score, norm, metric, qualitymetric, signal quality condition, statistical characteristics,discrimination score, neural network, deep learning network, machinelearning, training, discrimination, weighted averaging, preprocessing,denoising, signal conditioning, filtering, time correction, timingcompensation, phase offset compensation, transformation, component-wiseoperation, feature extraction, finite state machine, and/or anotherscore. The characteristics and/or STI may be determined/computed basedon the similarity score.

Any threshold may be pre-determined, adaptively (and/or dynamically)determined and/or determined by a finite state machine. The adaptivedetermination may be based on time, space, location, antenna, path,link, state, battery life, remaining battery life, available power,available computational resources, available network bandwidth, etc.

A threshold to be applied to a test statistics to differentiate twoevents (or two conditions, or two situations, or two states), A and B,may be determined. Data (e.g. CI, channel state information (CSI), powerparameter) may be collected under A and/or under B in a trainingsituation. The test statistics may be computed based on the data.Distributions of the test statistics under A may be compared withdistributions of the test statistics under B (reference distribution),and the threshold may be chosen according to some criteria. The criteriamay comprise: maximum likelihood (ML), maximum aposterior probability(MAP), discriminative training, minimum Type 1 error for a given Type 2error, minimum Type 2 error for a given Type 1 error, and/or othercriteria (e.g. a quality criterion, signal quality condition). Thethreshold may be adjusted to achieve different sensitivity to the A, Band/or another event/condition/situation/state. The threshold adjustmentmay be automatic, semi-automatic and/or manual. The threshold adjustmentmay be applied once, sometimes, often, periodically, repeatedly,occasionally, sporadically, and/or on demand. The threshold adjustmentmay be adaptive (and/or dynamically adjusted). The threshold adjustmentmay depend on the object, object movement/location/direction/action,object characteristics/STI/size/property/trait/habit/behavior, thevenue, feature/fixture/furniture/barrier/material/machine/livingthing/thing/object/boundary/surface/medium that is in/at/of the venue,map, constraint of the map (or environmental model), theevent/state/situation/condition, time, timing, duration, current state,past history, user, and/or a personal preference, etc.

A stopping criterion (or skipping or bypassing or blocking or pausing orpassing or rejecting criterion) of an iterative algorithm may be thatchange of a current parameter (e.g. offset value) in the updating in aniteration is less than a threshold. The threshold may be 0.5, 1, 1.5, 2,or another number. The threshold may be adaptive (and/or dynamicallyadjusted). It may change as the iteration progresses. For the offsetvalue, the adaptive threshold may be determined based on the task,particular value of the first time, the current time offset value, theregression window, the regression analysis, the regression function, theregression error, the convexity of the regression function, and/or aniteration number.

The local extremum may be determined as the corresponding extremum ofthe regression function in the regression window. The local extremum maybe determined based on a set of time offset values in the regressionwindow and a set of associated regression function values. Each of theset of associated regression function values associated with the set oftime offset values may be within a range from the corresponding extremumof the regression function in the regression window.

The searching for a local extremum may comprise robust search,minimization, maximization, optimization, statistical optimization, dualoptimization, constraint optimization, convex optimization, globaloptimization, local optimization an energy minimization, linearregression, quadratic regression, higher order regression, linearprogramming, nonlinear programming, stochastic programming,combinatorial optimization, constraint programming, constraintsatisfaction, calculus of variations, optimal control, dynamicprogramming, mathematical programming, multi-objective optimization,multi-modal optimization, disjunctive programming, space mapping,infinite-dimensional optimization, heuristics, metaheuristics, convexprogramming, semidefinite programming, conic programming, coneprogramming, integer programming, quadratic programming, fractionalprogramming, numerical analysis, simplex algorithm, iterative method,gradient descent, subgradient method, coordinate descent, conjugategradient method, Newton's algorithm, sequential quadratic programming,interior point method, ellipsoid method, reduced gradient method,quasi-Newton method, simultaneous perturbation stochastic approximation,interpolation method, pattern search method, line search,non-differentiable optimization, genetic algorithm, evolutionaryalgorithm, dynamic relaxation, hill climbing, particle swarmoptimization, gravitation search algorithm, simulated annealing, memeticalgorithm, differential evolution, dynamic relaxation, stochastictunneling, Tabu search, reactive search optimization, curve fitting,least square, simulation based optimization, variational calculus,and/or variant. The search for local extremum may be associated with anobjective function, loss function, cost function, utility function,fitness function, energy function, and/or an energy function.

Regression may be performed using regression function to fit sampleddata (e.g. CI, feature of CI, component of CI) or another function (e.g.autocorrelation function) in a regression window. In at least oneiteration, a length of the regression window and/or a location of theregression window may change. The regression function may be linearfunction, quadratic function, cubic function, polynomial function,and/or another function. The regression analysis may minimize at leastone of: error, aggregate error, component error, error in projectiondomain, error in selected axes, error in selected orthogonal axes,absolute error, square error, absolute deviation, square deviation,higher order error (e.g. third order, fourth order), robust error (e.g.square error for smaller error magnitude and absolute error for largererror magnitude, or first kind of error for smaller error magnitude andsecond kind of error for larger error magnitude), another error,weighted sum (or weighted mean) of absolute/square error (e.g. forwireless transmitter with multiple antennas and wireless receiver withmultiple antennas, each pair of transmitter antenna and receiver antennaform a link), mean absolute error, mean square error, mean absolutedeviation, and/or mean square deviation. Error associated with differentlinks may have different weights. One possibility is that some linksand/or some components with larger noise or lower signal quality metricmay have smaller or bigger weight), weighted sum of square error,weighted sum of higher order error, weighted sum of robust error,weighted sum of the another error, absolute cost, square cost, higherorder cost, robust cost, another cost, weighted sum of absolute cost,weighted sum of square cost, weighted sum of higher order cost, weightedsum of robust cost, and/or weighted sum of another cost. The regressionerror determined may be an absolute error, square error, higher ordererror, robust error, yet another error, weighted sum of absolute error,weighted sum of square error, weighted sum of higher order error,weighted sum of robust error, and/or weighted sum of the yet anothererror.

The time offset associated with maximum regression error (or minimumregression error) of the regression function with respect to theparticular function in the regression window may become the updatedcurrent time offset in the iteration.

A local extremum may be searched based on a quantity comprising adifference of two different errors (e.g. a difference between absoluteerror and square error). Each of the two different errors may comprisean absolute error, square error, higher order error, robust error,another error, weighted sum of absolute error, weighted sum of squareerror, weighted sum of higher order error, weighted sum of robust error,and/or weighted sum of the another error.

The quantity may be compared with a reference data or a referencedistribution, such as an F-distribution, central F-distribution, anotherstatistical distribution, threshold, threshold associated withprobability/histogram, threshold associated with probability/histogramof finding false peak, threshold associated with the F-distribution,threshold associated the central F-distribution, and/or thresholdassociated with the another statistical distribution.

The regression window may be determined based on at least one of: themovement (e.g. change in position/location) of the object, quantityassociated with the object, the at least one characteristics and/or STIof the object associated with the movement of the object, estimatedlocation of the local extremum, noise characteristics, estimated noisecharacteristics, signal quality metric, F-distribution, centralF-distribution, another statistical distribution, threshold, presetthreshold, threshold associated with probability/histogram, thresholdassociated with desired probability, threshold associated withprobability of finding false peak, threshold associated with theF-distribution, threshold associated the central F-distribution,threshold associated with the another statistical distribution,condition that quantity at the window center is largest within theregression window, condition that the quantity at the window center islargest within the regression window, condition that there is only oneof the local extremum of the particular function for the particularvalue of the first time in the regression window, another regressionwindow, and/or another condition.

The width of the regression window may be determined based on theparticular local extremum to be searched. The local extremum maycomprise first local maximum, second local maximum, higher order localmaximum, first local maximum with positive time offset value, secondlocal maximum with positive time offset value, higher local maximum withpositive time offset value, first local maximum with negative timeoffset value, second local maximum with negative time offset value,higher local maximum with negative time offset value, first localminimum, second local minimum, higher local minimum, first local minimumwith positive time offset value, second local minimum with positive timeoffset value, higher local minimum with positive time offset value,first local minimum with negative time offset value, second localminimum with negative time offset value, higher local minimum withnegative time offset value, first local extremum, second local extremum,higher local extremum, first local extremum with positive time offsetvalue, second local extremum with positive time offset value, higherlocal extremum with positive time offset value, first local extremumwith negative time offset value, second local extremum with negativetime offset value, and/or higher local extremum with negative timeoffset value.

A current parameter (e.g. time offset value) may be initialized based ona target value, target profile, trend, past trend, current trend, targetspeed, speed profile, target speed profile, past speed trend, the motionor movement (e.g. change in position/location) of the object, at leastone characteristics and/or STI of the object associated with themovement of object, positional quantity of the object, initial speed ofthe object associated with the movement of the object, predefined value,initial width of the regression window, time duration, value based oncarrier frequency of the signal, value based on subcarrier frequency ofthe signal, bandwidth of the signal, amount of antennas associated withthe channel, noise characteristics, signal h metric, and/or an adaptive(and/or dynamically adjusted) value. The current time offset may be atthe center, on the left side, on the right side, and/or at another fixedrelative location, of the regression window.

In the presentation, information may be displayed with a map (orenvironmental model) of the venue. The information may comprise:location, zone, region, area, coverage area, corrected location,approximate location, location with respect to (w.r.t.) a map of thevenue, location w.r.t. a segmentation of the venue, direction, path,path w.r.t. the map and/or the segmentation, trace (e.g. location withina time window such as the past 5 seconds, or past 10 seconds; the timewindow duration may be adjusted adaptively (and/or dynamically); thetime window duration may be adaptively (and/or dynamically) adjustedw.r.t. speed, acceleration, etc.), history of a path, approximateregions/zones along a path, history/summary of past locations, historyof past locations of interest, frequently-visited areas, customertraffic, crowd distribution, crowd behavior, crowd control information,speed, acceleration, motion statistics, breathing rate, heart rate,presence/absence of motion, presence/absence of people or pets orobject, presence/absence of vital sign, gesture, gesture control(control of devices using gesture), location-based gesture control,information of a location-based operation, identity (ID) or identifierof the respect object (e.g. pet, person, self-guided machine/device,vehicle, drone, car, boat, bicycle, self-guided vehicle, machine withfan, air-conditioner, TV, machine with movable part), identification ofa user (e.g. person), information of the user,location/speed/acceleration/direction/motion/gesture/gesturecontrol/motion trace of the user, ID or identifier of the user, activityof the user, state of the user, sleeping/resting characteristics of theuser, emotional state of the user, vital sign of the user, environmentinformation of the venue, weather information of the venue, earthquake,explosion, storm, rain, fire, temperature, collision, impact, vibration,event, door-open event, door-close event, window-open event,window-close event, fall-down event, burning event, freezing event,water-related event, wind-related event, air-movement event, accidentevent, pseudo-periodic event (e.g. running on treadmill, jumping up anddown, skipping rope, somersault, etc.), repeated event, crowd event,vehicle event, gesture of the user (e.g. hand gesture, arm gesture, footgesture, leg gesture, body gesture, head gesture, face gesture, mouthgesture, eye gesture, etc.). The location may be 2-dimensional (e.g.with 2D coordinates), 3-dimensional (e.g. with 3D coordinates). Thelocation may be relative (e.g. w.r.t. a map or environmental model) orrelational (e.g. halfway between point A and point B, around a corner,up the stairs, on top of table, at the ceiling, on the floor, on a sofa,close to point A, a distance R from point A, within a radius of R frompoint A, etc.). The location may be expressed in rectangular coordinate,polar coordinate, and/or another representation.

The information (e.g. location) may be marked with at least one symbol.The symbol may be time varying. The symbol may be flashing and/orpulsating with or without changing color/intensity. The size may changeover time. The orientation of the symbol may change over time. Thesymbol may be a number that reflects an instantaneous quantity (e.g.vital sign/breathing rate/heart rate/gesture/state/status/action/motionof a user, temperature, network traffic, network connectivity, status ofa device/machine, remaining power of a device, status of the device,etc.). The rate of change, the size, the orientation, the color, theintensity and/or the symbol may reflect the respective motion. Theinformation may be disclosed visually and/or described verbally (e.g.using pre-recorded voice, or voice synthesis). The information may bedescribed in text. The information may also be disclosed in a mechanicalway (e.g. an animated gadget, a movement of a movable part).

The user-interface (UI) device may be a smart phone (e.g. iPhone,Android phone), tablet (e.g. iPad), laptop (e.g. notebook computer),personal computer (PC), device with graphical user interface (GUI),smart speaker, device with voice/audio/speaker capability, virtualreality (VR) device, augmented reality (AR) device, smart car, displayin the car, voice assistant, voice assistant in a car, etc. The map (orenvironmental model) may be 2-dimensional, 3-dimensional and/orhigher-dimensional. (e.g. a time varying 2D/3D map/environmental model)Walls, windows, doors, entrances, exits, forbidden areas may be markedon the map or the model. The map may comprise floor plan of a facility.The map or model may have one or more layers (overlays). The map/modelmay be a maintenance map/model comprising water pipes, gas pipes,wiring, cabling, air ducts, crawl-space, ceiling layout, and/orunderground layout. The venue may be segmented/subdivided/zoned/groupedinto multiple zones/regions/geographicregions/sectors/sections/territories/districts/precincts/localities/neighborhoods/areas/stretches/expansesuch as bedroom, living room, storage room, walkway, kitchen, diningroom, foyer, garage, first floor, second floor, rest room, offices,conference room, reception area, various office areas, various warehouseregions, various facility areas, etc. The segments/regions/areas may bedisclosed in a map/model. Different regions may be color-coded.Different regions may be disclosed with a characteristic (e.g. color,brightness, color intensity, texture, animation, flashing, flashingrate, etc.). Logical segmentation of the venue may be done using the atleast one heterogeneous Type 2 device, or a server (e.g. hub device), ora cloud server, etc.

Here is an example of the disclosed system, apparatus, and method.Stephen and his family want to install the disclosed wireless motiondetection system to detect motion in their 2000 sqft two-storey townhouse in Seattle, Wash. Because his house has two storeys, Stephendecided to use one Type 2 device (named A) and two Type 1 devices (namedB and C) in the ground floor. His ground floor has predominantly threerooms: kitchen, dining room and living room arranged in a straight line,with the dining room in the middle. The kitchen and the living rooms areon opposite end of the house. He put the Type 2 device (A) in the diningroom, and put one Type 1 device (B) in the kitchen and the other Type 1device (C) in the living room. With this placement of the devices, he ispractically partitioning the ground floor into 3 zones (dining room,living room and kitchen) using the motion detection system. When motionis detected by the AB pair and the AC pair, the system would analyze themotion information and associate the motion with one of the 3 zones.

When Stephen and his family go out on weekends (e.g. to go for a campduring a long weekend), Stephen would use a mobile phone app (e.g.Android phone app or iPhone app) to turn on the motion detection system.When the system detects motion, a warning signal is sent to Stephen(e.g. an SMS text message, an email, a push message to the mobile phoneapp, etc.). If Stephen pays a monthly fee (e.g. $10/month), a servicecompany (e.g. security company) will receive the warning signal throughwired network (e.g. broadband) or wireless network (e.g. home WiFi, LTE,3G, 2.5G, etc.) and perform a security procedure for Stephen (e.g. callhim to verify any problem, send someone to check on the house, contactthe police on behalf of Stephen, etc.). Stephen loves his aging motherand cares about her well-being when she is alone in the house. When themother is alone in the house while the rest of the family is out (e.g.go to work, or shopping, or go on vacation), Stephen would turn on themotion detection system using his mobile app to ensure the mother is ok.He then uses the mobile app to monitor his mother's movement in thehouse. When Stephen uses the mobile app to see that the mother is movingaround the house among the 3 regions, according to her daily routine,Stephen knows that his mother is doing ok. Stephen is thankful that themotion detection system can help him monitor his mother's well-beingwhile he is away from the house.

On a typical day, the mother would wake up at around 7 AM. She wouldcook her breakfast in the kitchen for about 20 minutes. Then she wouldeat the breakfast in the dining room for about 30 minutes. Then shewould do her daily exercise in the living room, before sitting down onthe sofa in the living room to watch her favorite TV show. The motiondetection system enables Stephen to see the timing of the movement ineach of the 3 regions of the house. When the motion agrees with thedaily routine, Stephen knows roughly that the mother should be doingfine. But when the motion pattern appears abnormal (e.g. there is nomotion until 10 AM, or she stayed in the kitchen for too long, or sheremains motionless for too long, etc.), Stephen suspects something iswrong and would call the mother to check on her. Stephen may even getsomeone (e.g. a family member, a neighbor, a paid personnel, a friend, asocial worker, a service provider) to check on his mother.

At some time, Stephen feels like repositioning the Type 2 device. Hesimply unplugs the device from the original AC power plug and plug itinto another AC power plug. He is happy that the wireless motiondetection system is plug-and-play and the repositioning does not affectthe operation of the system. Upon powering up, it works right away.Sometime later, Stephen is convinced that the disclosed wireless motiondetection system can really detect motion with very high accuracy andvery low alarm, and he really can use the mobile app to monitor themotion in the ground floor. He decides to install a similar setup (i.e.one Type 2 device and two Type 1 devices) in the second floor to monitorthe bedrooms in the second floor. Once again, he finds that the systemset up is extremely easy as he simply needs to plug the Type 2 deviceand the Type 1 devices into the AC power plug in the second floor. Nospecial installation is needed. And he can use the same mobile app tomonitor motion in the ground floor and the second floor. Each Type 2device in the ground floor/second floor can interact with all the Type 1devices in both the ground floor and the second floor. Stephen is happyto see that, as he doubles his investment in the Type 1 and Type 2devices, he has more than double the capability of the combined systems.

According to various embodiments, each CI (CI) may comprise at least oneof: channel state information (CSI), frequency domain CSI, frequencyrepresentation of CSI, frequency domain CSI associated with at least onesub-band, time domain CSI, CSI in domain, channel response, estimatedchannel response, channel impulse response (CIR), channel frequencyresponse (CFR), channel characteristics, channel filter response, CSI ofthe wireless multipath channel, information of the wireless multipathchannel, timestamp, auxiliary information, data, meta data, user data,account data, access data, security data, session data, status data,supervisory data, household data, identity (ID), identifier, devicedata, network data, neighborhood data, environment data, real-time data,sensor data, stored data, encrypted data, compressed data, protecteddata, and/or another CI. In one embodiment, the disclosed system hashardware components (e.g. wireless transmitter/receiver with antenna,analog circuitry, power supply, processor, memory) and correspondingsoftware components. According to various embodiments of the presentteaching, the disclosed system includes Bot (referred to as a Type 1device) and Origin (referred to as a Type 2 device) for vital signdetection and monitoring. Each device comprises a transceiver, aprocessor and a memory.

The disclosed system can be applied in many cases. In one example, theType 1 device (transmitter) may be a small WiFi-enabled device restingon the table. It may also be a WiFi-enabled television (TV), set-top box(STB), a smart speaker (e.g. Amazon echo), a smart refrigerator, a smartmicrowave oven, a mesh network router, a mesh network satellite, a smartphone, a computer, a tablet, a smart plug, etc. In one example, the Type2 (receiver) may be a WiFi-enabled device resting on the table. It mayalso be a WiFi-enabled television (TV), set-top box (STB), a smartspeaker (e.g. Amazon echo), a smart refrigerator, a smart microwaveoven, a mesh network router, a mesh network satellite, a smart phone, acomputer, a tablet, a smart plug, etc. The Type 1 device and Type 2devices may be placed in/near a conference room to count people. TheType 1 device and Type 2 devices may be in a well-being monitoringsystem for older adults to monitor their daily activities and any signof symptoms (e.g. dementia, Alzheimer's disease). The Type 1 device andType 2 device may be used in baby monitors to monitor the vital signs(breathing) of a living baby. The Type 1 device and Type 2 devices maybe placed in bedrooms to monitor quality of sleep and any sleep apnea.The Type 1 device and Type 2 devices may be placed in cars to monitorwell-being of passengers and driver, detect any sleeping of driver anddetect any babies left in a car. The Type 1 device and Type 2 devicesmay be used in logistics to prevent human trafficking by monitoring anyhuman hidden in trucks and containers. The Type 1 device and Type 2devices may be deployed by emergency service at disaster area to searchfor trapped victims in debris. The Type 1 device and Type 2 devices maybe deployed in an area to detect breathing of any intruders. There arenumerous applications of wireless breathing monitoring withoutwearables.

Hardware modules may be constructed to contain the Type 1 transceiverand/or the Type 2 transceiver. The hardware modules may be sold to/usedby variable brands to design, build and sell final commercial products.Products using the disclosed system and/or method may be home/officesecurity products, sleep monitoring products, WiFi products, meshproducts, TV, STB, entertainment system, HiFi, speaker, home appliance,lamps, stoves, oven, microwave oven, table, chair, bed, shelves, tools,utensils, torches, vacuum cleaner, smoke detector, sofa, piano, fan,door, window, door/window handle, locks, smoke detectors, caraccessories, computing devices, office devices, air conditioner, heater,pipes, connectors, surveillance camera, access point, computing devices,mobile devices, LTE devices, 3G/4G/5G/6G devices, UMTS devices, 3GPPdevices, GSM devices, EDGE devices, TDMA devices, FDMA devices, CDMAdevices, WCDMA devices, TD-SCDMA devices, gaming devices, eyeglasses,glass panels, VR goggles, necklace, watch, waist band, belt, wallet,pen, hat, wearables, implantable device, tags, parking tickets, smartphones, etc.

The summary may comprise: analytics, output response, selected timewindow, subsampling, transform, and/or projection. The presenting maycomprise presenting at least one of: monthly/weekly/daily view,simplified/detailed view, cross-sectional view, small/large form-factorview, color-coded view, comparative view, summary view, animation, webview, voice announcement, and another presentation related to theperiodic/repetition characteristics of the repeating motion.

A Type 1/Type 2 device may be an antenna, a device with antenna, adevice with a housing (e.g. for radio, antenna, data/signal processingunit, wireless IC, circuits), device that has interface toattach/connect to/link antenna, device that is interfaced to/attachedto/connected to/linked to anotherdevice/system/computer/phone/network/data aggregator, device with a userinterface (UI)/graphical UI/display, device with wireless transceiver,device with wireless transmitter, device with wireless receiver,internet-of-thing (IoT) device, device with wireless network, devicewith both wired networking and wireless networking capability, devicewith wireless integrated circuit (IC), Wi-Fi device, device with Wi-Fichip (e.g. 802.11a/b/g/n/ac/ax standard compliant), Wi-Fi access point(AP), Wi-Fi client, Wi-Fi router, Wi-Fi repeater, Wi-Fi hub, Wi-Fi meshnetwork router/hub/AP, wireless mesh network router, adhoc networkdevice, wireless mesh network device, mobile device (e.g.2G/2.5G/3G/3.5G/4G/LTE/5G/6G/7G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA,CDMA, WCDMA, TD-SCDMA), cellular device, base station, mobile networkbase station, mobile network hub, mobile network compatible device, LTEdevice, device with LTE module, mobile module (e.g. circuit board withmobile-enabling chip (IC) such as Wi-Fi chip, LTE chip, BLE chip), Wi-Fichip (IC), LTE chip, BLE chip, device with mobile module, smart phone,companion device (e.g. dongle, attachment, plugin) for smart phones,dedicated device, plug-in device, AC-powered device, battery-powereddevice, device with processor/memory/set of instructions, smartdevice/gadget/items: clock, stationary, pen, user-interface, paper, mat,camera, television (TV), set-top-box, microphone, speaker, refrigerator,oven, machine, phone, wallet, furniture, door, window, ceiling, floor,wall, table, chair, bed, night-stand, air-conditioner, heater, pipe,duct, cable, carpet, decoration, gadget, USB device, plug, dongle,lamp/light, tile, ornament, bottle, vehicle, car, AGV, drone, robot,laptop, tablet, computer, harddisk, network card, instrument, racket,ball, shoe, wearable, clothing, glasses, hat, necklace, food, pill,small device that moves in the body of creature (e.g. in blood vessels,in lymph fluid, digestive system), and/or another device. The Type 1device and/or Type 2 device may be communicatively coupled with: theinternet, another device with access to internet (e.g. smart phone),cloud server (e.g. hub device), edge server, local server, and/orstorage. The Type 1 device and/or the Type 2 device may operate withlocal control, can be controlled by another device via a wired/wirelessconnection, can operate automatically, or can be controlled by a centralsystem that is remote (e.g. away from home).

In one embodiment, a Type B device may be a transceiver that may performas both Origin (a Type 2 device, a Rx device) and Bot (a Type 1 device,a Tx device), i.e., a Type B device may be both Type 1 (Tx) and Type 2(Rx) devices (e.g. simultaneously or alternately), for example, meshdevices, a mesh router, etc. In one embodiment, a Type A device may be atransceiver that may only function as Bot (a Tx device), i.e., Type 1device only or Tx only, e.g., simple IoT devices. It may have thecapability of Origin (Type 2 device, Rx device), but somehow it isfunctioning only as Bot in the embodiment. All the Type A and Type Bdevices form a tree structure. The root may be a Type B device withnetwork (e.g. internet) access. For example, it may be connected tobroadband service through a wired connection (e.g. Ethernet, cablemodem, ADSL/HDSL modem) connection or a wireless connection (e.g. LTE,3G/4G/5G, WiFi, Bluetooth, microwave link, satellite link, etc.). In oneembodiment, all the Type A devices are leaf node. Each Type B device maybe the root node, non-leaf node, or leaf node.

Wireless detection of respiration rates is crucial for manyapplications. Most of the state-of-art solutions estimate breathingrates with the prior knowledge of crowd numbers as well as assuming thedistinct breathing rates of different users, which is neither naturalnor realistic. However, few of them can leverage the estimated breathingrates to recognize human subjects (a.k.a, identity matching). In thisdisclosure, using the channel state information (CSI) of a single pairof commercial WiFi devices, a novel system is disclosed to continuouslytrack the breathing rates of multiple persons without such impracticalassumptions. The disclosed solution includes an adaptive subcarriercombination method that boosts the signal to noise ratio (SNR) ofbreathing signals, and an iterative dynamic programming and a traceconcatenating algorithm that continuously track the breathing rates ofmultiple users. By leveraging both the spectrum and time diversity ofthe CSI, the disclosed system can correctly extract the breathing ratetraces even if some of them merge together for a short time period.Furthermore, by utilizing the breathing traces obtained, the disclosedsystem can do people counting and recognition simultaneously. Extensiveexperiments are conducted in two environments (an on-campus lab and acar). The results show that 86% of average accuracy can be achieved forpeople counting up to 4 people for both cases. For 97.9% out of all thetesting cases, the absolute error of crowd number estimates is within 1.The system achieves an average accuracy of 85.78% for people recognitionin a smart home case.

In this disclosure, the disclosed solution can continuously track humanrespiration rate without any prior knowledge of the crowd number orassuming that the breathing rates of different users are distinct. Thedisclosed system focuses on matching the breathing rates estimated indifferent time instances to different users, i.e., which breathing ratecorresponds to which person. By utilizing the estimated breathing ratetraces, the disclosed system can achieve people counting as well asrecognition at the same time. Achieving identity matching based onmulti-person breathing rates for both purposes, however, entails severalunique challenges.

First, the subtle changes caused by human breathing could be easilyundermined by measurement noises in WiFi CSI. The situation becomes evenworse in multi-person scenarios. To overcome the problem, one canleverage the diversity residing in subcarriers and multiple links(antenna pairs) to reduce noises while boosting the breathing signals.One can show that, by properly combining different subcarriers andlinks, the breathing signals can be considerably better enhanced thanwhat could be achieved by any individual or the average of them.

Second, a person's breathing rate varies over time, making itnon-trivial to associate the successive breathing rate estimates to thecorresponding persons. Considering that one's breathing rate will notfluctuate within a short time period, one can build a Markov chain modelfor the natural breathing signals and further employ an iterativedynamic programming algorithm to continuously track multiple breathingtraces (i.e., sequences of breathing rates of different users).

Third, the number of users may not be fixed for an area of interest,since users might come and go. In order to output real-time estimates ofthe occupancy level, the present teaching discloses a traceconcatenating algorithm to maintain the traces of existing users, whichconcatenates the latest estimates with the disclosed traces to determineexisting users, newly arriving users, and leaving users.

Lastly, although the human respiration rate varies in a small range,breathing pattern of different individuals are distinct. Based on thisobservation, one can build a database of breathing traces and applyhypothesis-testing to do human recognition for the smart home scenario.

One can prototype the disclosed system using a pair of commodityoff-the-shelf (COTS) WiFi devices and conduct experiments in two typicaltargeted environments (i.e., a lab office and a car), where 12 users areinvolved in the evaluation. The average accuracy of people counting upto 4 people is more than 86% for both environments. For 97.9% out of allthe testing cases, the absolute error of crowd number estimates iswithin 1. The disclosed system achieves similar performance for bothenvironments, demonstrating the independence on environment, whichoutperforms the training based method. Lastly, one can investigate theperformance of people recognition using breathing traces for a 4-peoplefamily. The system can achieve 85.78% accuracy. The disclosed approachmakes an important step towards smart home applications in terms ofbreathing rate estimation, passive people counting and humanrecognition.

The main contributions of this work are as follows. One can devise apipeline of novel techniques to estimate the breathing traces ofmultiple users, including a subcarrier combination method to enhancemultiple breathing signals, an iterative dynamic programming and traceconcatenating algorithms to continuously track successive breathingrates of multiple users using the CSI of COTS WiFi devices. Anon-intrusive training-free method is disclosed to estimate the crowdnumber using the extracted breathing traces. Furthermore, ahypothesis-testing model is disclosed to leverage the breathing tracesto do human recognition. One can prototype and evaluate the disclosedsystem on COTS WiFi devices. The results in indoor spaces and in a cardemonstrate promising performance.

The computational pipeline underlying the disclosed system is shown inFIG. 1. Different from many previous works aiming at estimatingindependent breathing rates at certain time instances, this work focuseson utilizing the frequency as well as time domain information to doidentity matching. The core idea is to estimate the breathing ratesequences along the time (a.k.a, breathing rate traces) of differentindividuals. Furthermore, utilizing the estimated breathing traces, onecan estimate the occupancy level as well as predict the user identity inthe observation area. This idea immediately leads to three stages of thedisclosed system: (1) Multi-user breathing spectrum generation, (2)breathing rate trace tracking, and (3) people counting and recognition.

In the first stage, the disclosed system first performs Short-TermFourier Transform (STFT) on CSI measurements to extract the periodicbreathing signals. As long as the breathing rates of differentindividuals are different, multiple frequency components would beobserved in the frequency response. The extracted breathing signals aretypically fairly weak on a single subcarrier, which are further boostedby a novel adaptive subcarrier combining method. Stage 1 finally outputsa spectrogram of the estimated breathing rates over time.

In Stage 2, the goal is to track the breathing rate traces (i.e.,breathing sources) from the spectragram obtained from Stage 1. However,there is a significant gap between breathing rates to breathing ratetraces because of two reasons: First, different individuals may have thesame breathing rates that overlap with each other. Second, one'sbreathing rate varies over time. To address the challenges, a MarkovChain Model is introduced to handle dynamics in natural breathing. Thepresent teaching discloses a successive cancellation scheme thatresolves each individual's breathing trace one by one by a novelalgorithm of iterative dynamic programming. Thereafter, one canconcatenate the identified traces of breathing rates in adjacent timewindows to further identify the arriving and leaving time of humansubjects.

In Stage 3, one can leverage the estimated breathing rate traces givenby Stage 2 to do people counting and recognition. By further utilizingthe time-domain information and removing outliers of the estimatedbreathing rate traces by a novel quasi-bilateral filter, the systemgives an estimate of the crowd number. Then by hypothesis testing, onecan do people identity recognition according to the extracted breathingrate traces.

Multi-User Breathing Spectrum Generation

CSI Model with Breathing Impact

The channel state information (CSI) depicts how radio signals propagatefrom a transmitter (Tx) to a receiver (Rx). In the presence of humanbeings, one or more paths of signal propagation will be altered due tohuman motion. Given a pair of Tx and Rx with multiple omnidirectionalantennas, the CSI of link m at time t and frequency ƒ_(k) is modeled as

$\begin{matrix}{{{h^{(m)}\left( {t,f_{k}} \right)} = {{\sum\limits_{l = 1}^{L}{{a_{l}(t)}{\exp \left( {{- j}\; 2\; \pi \; f_{k}\frac{d_{l}(t)}{c}} \right)}}} + {n\left( {t,f_{k}} \right)}}},} & (1)\end{matrix}$

where k∈

is the subcarrier (SC) index with center frequency ƒ_(k) in the set ofusable SCs

. L is the total number of multipath components (MPCs), while a_(l)(t)and d_(l)(t) denote the complex gain and propagation length of MPC l.n(t, ƒ_(k)) is the additive white noise, and c is the speed of light.

In the presence of breathing, (1) can be rewritten as

$\begin{matrix}{{{h^{(m)}\left( {t,f_{k}} \right)} = {{\sum\limits_{i \in }{\sum\limits_{l \in {\Omega_{d_{i}}{(t)}}}{{a_{l}(t)}{\exp \left( {{- j}\; 2\pi \; f_{k}\frac{d_{l}(t)}{c}} \right)}}}} + {\sum\limits_{l \in \Omega_{s}}{a_{l}{\exp \left( {{- j}\; 2\pi \; f\frac{d_{l}}{c}} \right)}}} + {n\left( {t,f_{k}} \right)}}},} & (2)\end{matrix}$

where

denotes the set of human subjects. Ω_(d) _(i) denotes the MPCs scatteredby human being i, resulting in time-variant complex gain and delay,Ω_(s) denotes the MPCs that are not affected by people's breathing,whose complex gain and delay keep time-invariant. The gain of MPCs inΩ_(d) _(i) could be modeled as

$\begin{matrix}{{{a_{l}(t)} = {a_{l} \times \left( {1 + {\frac{\Delta \; d_{l}}{d_{l}}\sin \; {{\theta sin}\left( {\frac{2\pi \; t}{T_{b_{i}}} + \varphi} \right)}}} \right)^{- \Psi}}},} & (3)\end{matrix}$

where a_(l) and d_(l) are gain and path length in a static environment,Δd_(l) is the difference of propagation length caused by chest movement,θ is the angle between human scatter and the EM wave, and ϕ is theinitial phase. Ψ is the path loss exponent. Since the chest movement ismuch smaller than the path length, i.e., Δd_(l)<<d_(l), the amplitude ofMPC in both Ω_(s) and Ω_(d) _(i) can be assumed to be time-invariant,e.g., a_(l)(t)≈a_(l), and the set of MPCs are assumed to be the same,i.e., Ω_(d) _(i) (t)≈Ω_(d) _(i) .

Boosting SNR of Breathing Signals

For each MPC subset Ω_(d) _(i) , the delay is periodic due to theperiodic chest movement, i.e., d_(l)(t+T_(b) _(i) )=d_(l)(t), ∀l∈Ω_(d)_(i) . Hence one would be able to see multiple frequency components ofthe measured CSI in frequency domain, each corresponding to a distinctbreathing signal.

The breathing signals can be extracted by applying Short-Term FourierTransform (STFT) to the CSI measurement. In specific, one can firstapply a sliding window of length W to the captured CSI time series ofeach SC in every link, and then obtain the frequency spectrum byperforming Fast Fourier Transform (FFT) over each time window. One canthen employ a bandpass filter on the spectrum to consider only thenormal range of human breathing frequencies [b_(min), b_(max)]. The FFTis performed on every SC to obtain the individual spectrum for all theN_(Tx)×N_(Rx)×N_(sc) SCs, where N_(Tx), N_(Rx), and N_(sc) are thenumber of Tx antennas, Rx antennas, and usable SCs on each Tx-Rx link,respectively.

As shown in FIG. 2, each breathing signal from one person contributes toone evident peak in the obtained power spectrum density (PSD). DifferentSCs experience diverse sensitivity levels to the identical breathingmotion. Previous approaches attempt to select a set of best SCs based onvariance, amplitude or ensemble average of CSI among all SCs to improveSNR. However, the following observations show the flaws of theseapproaches: 1) The response power of different SCs to the same breathingsource is different (See columns in FIG. 2). 2) For the same SC, theresponse power to different breathing sources is different (See rows inFIG. 2). 3) The response power of different links is distinct (Differentfigures in FIG. 2). Therefore, there is no single SC that is universallysensitive to all breathing sources. Using the same subset of SCs fordifferent frequency components may not produce equally good SNR for allbreathing signals. Furthermore, using a universal threshold for alllinks may lose information from links with low response power.

Inspired by these observations, one can first use a novel adaptive SCcombining criteria to boost the SNR of breathing signal of each link.For link m, the selected SCs for a given frequency component q satisfythe condition that

$\begin{matrix}{{{E_{k}^{(m)}(q)} \geq {\alpha {\max\limits_{q \in {Q\; i} \in V}\left\{ {E^{(m)}\left( {q,i} \right)} \right\}}}},{\forall{k \in V}},} & (4)\end{matrix}$

where Q is the set of frequency components in the range of [b_(min),b_(max)]. E_(k) ^((m))(q) denotes the power of the k-th SC over link mat frequency component q and

{E^((m))(q, i)} denotes the maximum power of link m over all frequencycomponents and SCs. α is a hyper-parameter which determines a relativethreshold th^((m))=

{E^((m))(q, i)} for SC selection. Note that th^((m)) is adaptive toindividual link quality, as inspired by the third observation above.Thus, the enhanced power of frequency component q in link m is

E ^((m))(q)←

E _(k) ^((m))(q)1(E _(k) ^((m))(q)≥th ^((m))).  (5)

To further incorporate diverse link quality, one can normalize the powerfor each link and then combine them together to further improve the SNR:

$\begin{matrix}{\left. {E^{(m)}(q)}\leftarrow\frac{E^{(m)}(q)}{\sum\limits_{i \in Q}{E^{(m)}(i)}} \right.,{\forall{q \in Q}},} & (6)\end{matrix}$E(q)←Σ_(m=1) ^(M) E ^((m))(q),∀q∈Q,  (7)

where E(q) is the power of frequency component q after link combinationand M=N_(Tx)×N_(Rx) is the total number of links.

FIG. 3 shows the effect of SC combination for several exemplary links.As seen, the disclosed SC selection and combination scheme (shown inblue curves) remarkably improves the SNR for the frequency components ofinterests, outperforming the simple average scheme (shown in redcurves). FIG. 4 further depicts the PSD after the combination of all 9links, which demonstrates that noise and interference have beeneffectively suppressed. The ground-truth of breathing rates are markedwith the black dashed lines. As a comparison, simple average of all SCssuffer from less dominant peaks for the desired breathing signals andfalse peaks.

Breathing Rate Trace Tracking

From Breathing Rates to Breathing Rate Traces

Previous works estimate the number of people by the number of candidatebreathing rates. However, they have several limitations. First, thebreathing rate estimation may not be accurate enough for a single timeinstance. Second, different users may have close breathing rates thatare indistinguishable from the frequency spectrum, resulting inpotential underestimation. Third, the number of people could vary overtime as people may come and go. And the accompanying motion will alsocorrupt the breathing signals.

To map the breathing rates to individuals and thus further estimate theaccurate crowd number, one can utilize the diversity in the time seriesof breathing rate estimates for reliable estimation. One can first modelthe dynamic breathing rates as a Markov process. Noting that thebreathing signals are periodic where breathing frequency can smoothlychange over time, the variation of breathing rate between two adjacenttime bins is assumed to follow a normal distribution

(0, σ²), with the probability density function (PDF) p(ƒ). Since theoperation of STFT automatically discretizes the continuous frequency inthe range of [b_(min), b_(max)] into |Q| frequency components, where |Q|means the cardinality of set Q, the natural beath can be modeled as aMarkov chain, and the transition probability matrix is denoted as P∈

^(|Q|)×

^(|Q|), which is defined as

$\begin{matrix}\begin{matrix}{{P\left( {q,q^{\prime}} \right)} = {P\left( {{g(i)} = {\left. q^{\prime} \middle| {g\left( {i - 1} \right)} \right. = q}} \right)}} \\{{= {\overset{{({q^{\prime} - q + \frac{1}{2}})}*\Delta \; f}{\int\limits_{{({q^{\prime} - q - \frac{1}{2}})}*\Delta \; f}}{{p(f)}{df}}}},}\end{matrix} & (8)\end{matrix}$

where ∀q, q′∈Q and g is a mapping indicating the frequency component ofthe breathing rate at given time slots.

To estimate the breathing rate trace in a given time slot t, thedisclosed system leverages the spectrum in [t−W, t], where W is thewindow length. An output is produced every W_(s) seconds, and thespectrum is updated at the same time. Thus to estimate the breathingtraces at time t, a spectrum S∈

₊ ^(I)×

₊ ^(|Q|) is leveraged, where

${I = \frac{W}{W_{s}}}.$

In principle, the breathing signal is more periodic than noise and othermotion interference. Thus, it is more likely to be observed as peaks inmost of the time, and thus the breathing signal will form a trace in thegiven spectrum along the time with the frequency changing slightly, asshown in FIG. 5. In the following, one can first extract the traces ofsuccessive breathing rates in the given window, and then concatenatethem over time.

Extracting Breathing Rate Traces

Theoretical Model

For a given spectrum S, a reasonable estimate of the breathing trace canbe obtained by

$\begin{matrix}{{g^{*} = {\underset{g}{argmax}{E(g)}}},} & (9)\end{matrix}$

where g indicates the breathing trace, denoted as

g=(g(n),n)_(n=1) ^(I).  (10)

Here, g:[1,I]→Q is a mapping indicating the frequency component of thetrace at the given time. E(g) is the power of a trace, defined as

E(g)=Σ_(i=1) ^(I) S(i,g(i)),  (11)

where S(i,j) denotes the power at time bin i and frequency component j.

Considering that one's breathing rate will not fluctuate a lot within ashort period, a regularization term is added to penalize sudden changesin frequencies of interests. A breathing trace is then a series ofbreathing rate estimates that achieve a good balance between frequencypower and temporal smoothness. The smoothness of a trace can beevaluated by a cost function C(g), defined as

C(g)

−log P(g(1))−Σ_(i=2) ^(I) log P(g(i−1),g(i)),  (12)

where the frequency transition probability P(g(i−1), g(i)) can becalculated by (8). Without loss of generality, one can assume a uniformprior distribution, i.e.,

${{P\left( {g(1)} \right)} = \frac{1}{Q}}.$

The cost function C(g) is the negative of the log-likelihood for a giventrace. The smoother a trace is, the larger its probability is, and thesmaller the cost it incurs.

The most probable breathing trace can be found by solving

$\begin{matrix}{{g^{*} = {{\underset{g}{argmax}{E(g)}} - {\lambda \; {C(g)}}}},} & (13)\end{matrix}$

where λ is a regularization factor. Here it is denoted E(g)−λC(g) as theregularized energy of trace g. By properly choosing the hyper-parameterλ, the system can ensure that the regularized energy of a true breathingtrace is positive, while when the observation area is empty, theregularized energy for any trace candidate in the given spectrum isnegative.

Iterative Dynamic Programming

The problem in (13) can be solved by dynamic programming. However,dynamic programming typically can only find the trace with the maximumregularized energy and cannot deal with multiple breathing traces. Thepresent teaching discloses a successive cancellation scheme to findmultiple traces one by one via a novel method of iterative dynamicprogramming (IDP).

The principle idea of the IDP is intuitive. For a given spectrum S, themost probable breathing trace is first found by dynamic programming. Tofurther determine if there are any other breathing traces, theidentified trace will be erased from the spectrum, and then a new roundof dynamic programming is performed to find another candidate trace.This successive cancellation procedure will be run iteratively untilthere is no more effective traces in the spectrum.

For clarity, (i, q) denotes the bin index with timestamp i and frequencycomponent q. One may want to find the best trace of frequency peaks fromtimestamp i to j, which is denoted as g_(i)

g_(j). Define the regularized energy of trace g_(i)

g_(j) that ends at point (j, n) as s(g_(i)

(j, n)). The disclosed approach is to search all possible traces g_(i)

(j, n) that end at frequency point n and select the best one among them.This can be achieved by finding the optimal traces for all the binsalong with the adjacent timestamps. For simplicity, one may denote theregularized energy at each bin as its score given by

$\begin{matrix}{{{s\left( {i,q} \right)} = {{S\left( {i,q} \right)} + {\max \left\{ {{s\left( {{i - 1},q^{\prime}} \right)} + {{\lambda log}\; {P\left( {q^{\prime},q} \right)}}} \right\}}}},{i = 2},{3\mspace{14mu} \ldots}\mspace{14mu},I,{\forall q},{q^{\prime} \in Q},} & (14)\end{matrix}$

where s(1, q)=S(1, q)+λ log P(g(1)=q). The score of a given bin is themaximum achievable regularized energy that it can obtain. In otherwords, it determines the optimal paths that pass through bin (i, q).

The entire optimal breathing trace can be found by backtracking the binsthat contribute to the maximum score g*(I) of the last timestamp. Forthe rest of the breathing trace in the observation window, i.e., ∀i=I−1,I−2, . . . , 1, one can have

$\begin{matrix}{{g^{*}(i)} = {{\underset{\forall{q \in Q}}{argmax}{s\left( {i,q} \right)}} + {{\lambda log}\; {{P\left( {q,{g^{*}\left( {i + 1} \right)}} \right)}.}}}} & (15)\end{matrix}$

The backtracking procedure in (15) gives the optimal trace g* for agiven spectrum, which is the optimal solution for (13).

To further check if there are any other candidate breathing signals inthe given spectrum, the trace g* should be removed. For the ideal case,one may only need to remove the bins along g*. However, since the numberof FFT points are limited, the energy of the breathing signal isdiffused around the center of breathing trace, which forms an energystrip in the given spectrum as shown in FIG. 3. Thus, if one may onlyremove the energy along the optimal trace g* and consecutively executedynamic programming in (14) and (15), one will get a group of tracesinside one energy strip. Therefore, IDP applies a windowing module onthe optimal trace g* to emulate the diffusing effect of FFT to get anenergy strip. The updated spectrum after one erases the optimal energystrip is

S(i)←S(i)−S(i,g*(i))*w, ∀i=1,2, . . . I,  (16)

where S(i) denotes the energy of spectrum at timestamp i, and w is thefrequency response of the windowing module. Operator * denotesconvolution operation, which can emulate the energy stripe caused by thediffusing effect of FFT.

One may recursively perform the above dynamic programming and spectrumcancellation to find multiple traces. The algorithm terminates when thescore of the found trace is negative, indicating an empty spectrumwithout any effective traces.

The procedure of iterative dynamic programming is summarized in FIG. 16.FIGS. 6A-6C illustrate the details of this finding-then-erasingprocedure, where 3 people sit in car doing natural breathing and theiraverage breathing rate are [10 14 15] bpm. In FIG. 6A, the trace foundby DP is marked by the line. The energy stripe of this trace is removedas shown in FIG. 6B. The spectrogram, when IDP terminates, is shown inFIG. 6C, and lines in the figure indicate the breathing traces. It isclear to see that although there is still some residual energy notperfectly removed, IDP terminates properly since there are no tracessatisfying the constraint of non-negative regularized energy.

Detecting Empty Case

Ideally, when there is no person present in the monitoring area, nobreathing trace would be picked up since the spectrum would be randomdue to the normal distribution of the thermal noise. In reality,however, false traces could be detected since some noise might beboosted in the empty case. To avoid this effect, one may employ motiondetection to determine empty cases. If no motion (not even chestmovement) is detected, the system will directly claim empty; otherwise,the above steps are performed to find a potential breathing rate trace.Here the motion detector needs to be sensitive and robust enough todetect breathing motion. In the present teaching, one may employ themotion statistics, which achieves almost zero false alarm.

Trace Concatenating

Iterative dynamic programming provides the breathing rate traces foreach time window. In practice, a continuous monitoring system, however,would operate for much longer time than a time window, posing extrainformation gains to enhance the trace extraction. In this part, thepresent teaching discloses a novel trace concatenating algorithm (FIG.16) to concatenate trace segments belonging to the same breathing signalin different time windows, which not only improves the trace segments,but also enables detection of the start and end time of each trace (orequivalently, the entering and leaving time of a specific user).

For clarity, one may store all disclosed traces in a database. The jthtrace found previously is denoted as g_(j) ^(pre)(t_(st):t_(end)), wherej=1, . . . , J and t_(st) and t_(end) denote the start and end time ofthe trace. The kth traces found in the current time window [t−W, t] isdenoted as g_(k)(t−W:t), where k=1, . . . , K. Furthermore, thesimilarity between two traces is defined as the ratio between theoverlapped time in the window and the window length, which is expressedas

$\begin{matrix}{{{f\left( {g_{j}^{pre},g_{k}} \right)} = \frac{{1\left( {{g_{j}^{pre}\left( {t_{st}:t_{end}} \right)} = {g_{k}\left( {t - {W:t}} \right)}} \right)}}{I - 1}},} & (17)\end{matrix}$

where ƒ(g_(j) ^(pre), g_(k))∈[0,1]. A similarity matrix F∈

^(J)×

^(K) can be calculated according to (17) to show the similarity betweenall the traces in the current window and those in the database. In orderto find the previous part for g_(k)(t−W:t), one may only need to findthe maximum item of f(k), which is the k-th column of F. The row indexof the maximum similarity indicates the index of the previous trace ifthe maximum similarity is above a predefined threshold.

If there exists a previous trace with a high enough similarity, it meansthat the corresponding breathing rate trace has been detected before.Then the endpoint of the corresponding trace should be updated. One maylet the endpoint be the current time and update the correspondingfrequency component accordingly. In case a new user arrives, there willbe no existing traces that have a similarity larger than the thresholdand thus a new trace is created with the corresponding timestamps andfrequency components. Similarly, no trace in the current window beingsimilar to the past traces corresponds to a user that has left, and thusthe trace would be terminated.

FIGS. 7A-7D and FIG. 8 show the effect of trace concatenating algorithm.Four adjacent time windows are shown in FIGS. 7A-7D, and traces found byIDP are marked by lines. One can see that although the breathing tracein the middle of the spectrogram is not detected in the second and thirdwindow (due to body motion as well as breathing rate change of thesubject), since the trace found in the fourth window still achieves highsimilarity with the trace found in the first window, it still can beconcatenated as shown in FIG. 8.

People Counting and Recognition

People Counting

IDP and trace concatenation provides the estimation of breathing ratetraces, and the trace number would be the estimate of occupancy level.Although the IDP and trace concatenating have considered the stabilityof human breath in the observation area, the estimation result may stillsuffer from large noise and have some false alarms or underestimationsfor a real-time output as shown in FIG. 7B, FIG. 7C. Toeliminate/mitigate these outliers for a real-time system, one may designa quasi-bilateral filter to explore the information contained in theprevious estimations. Similar to the bilateral filter, the designedfilter considers the distance in time and estimation domainsimultaneously, but one may make some improvements according to thedisclosed system. First, for a real-time system, the filter can only usethe past and current estimation result. Furthermore, since IDP and traceconcatenation leverage time as well as frequency information to getcontinuous breathing rate traces, the preliminary estimations areconsistent in a short period. Thus, if one may directly use a bilateralfilter, only the first incorrect output will be rectified. Given thesetwo constraints, one may develop a segment-based filter, where eachsegment is a consistent preliminary estimation sequence.

Specifically, the output is determined by the current estimation and theprevious segments. One may denote the weight of segment s as W_(seg)(S)expressed as

W _(seg)(s)=w(l _(s))*w(τ_(s))*w(d _(s))  (18)

where l_(s) is the length of segment s, and τ_(s) is the time differencebetween segment s and current timestamp. d_(s) is the estimationdifference between current estimation and segment s as shown in FIG. 9.Intuitively, the longer the segment is, the greater weight will beassigned. In contrary, the larger the distance is, no matter in time orthe estimate of the crowd number, the smaller the influence of thissegment imposing on the current result. For clarity, the set of segmentswith i estimated people is denoted as S_(i), and the current estimatednumber as j. The weight that the currently estimated people is i afterquasi-bilateral filter can be calculated by

$\begin{matrix}{\left. {p(i)}\leftarrow{\frac{1}{N}\left( {\sum\limits_{s \in S_{i}}\frac{I_{s}}{\tau_{s}}} \right)e^{- d_{s}}} \right.,} & (19)\end{matrix}$

where N is the total number of segments, the estimation difference isd_(s)=|i−j|, and W_(seg)(s) in (18) is designed as

$\begin{matrix}\left. {W_{seg}(s)}\leftarrow{\frac{I_{s}}{\tau_{s}}{e^{- d_{s}}.}} \right. & (20)\end{matrix}$

The eventual result after filtering is j′, given by

$\begin{matrix}{j^{\prime} = {\underset{i}{argmax}{p(i)}}} & (21)\end{matrix}$

FIG. 9 shows the estimation results before and after quasi-bilateralfiltering. Clearly, the novel quasi-bilateral filter can remove theestimation outliers effectively, and thus improve the performance ofpeople counting system.

Human Recognition in Smart Homes.

In this part, one may apply the disclosed respiration tracking result toan interesting smart home application, where one may want to recognizehuman identity in the observation area. Based on observations, althoughat some time instances, the breathing rate of different people can bethe same, for a specific person, his or her breathing rate tends tolocate in a certain range, which is a quite unique feature forhuman/user recognition. In other words, the breathing distribution ofdifferent people tends to be different. Motivated by this observation,one may utilize hypothesis-testing to do human recognition leveragingthe breathing traces obtained.

The probability density function (PDF) of each person is assumed tofollow a Gaussian distribution, i.e., p_(k)(θ)˜

(μ_(k), σ_(k)), where k indicates the label of the testing subject. Toobtain the breathing rate distribution of different people, one mayfirst get the rough PDF estimation from the histogram in the trainingdataset. This histogram will be fitted into a Gaussian model to get PDFfor each testing subject. FIG. 10 shows exemplary breathing PDFs for 4subjects. Based on the PDF distribution, for a certain observation ofbreathing trace g, the log-likelihood of the trace belonging to subjectk can be calculated by

(k)=Σ_(n=1) ^(N) log(p _(k)(g(n))),  (22)

where N is the number of time instances of a given trace. The predictedlabel should be the one that achieves the maximum likelihood, i.e.,

$\begin{matrix}{K = {\underset{k}{{argmax}\;}{{\mathcal{L}(k)}.}}} & (23)\end{matrix}$

Experiments and Evaluation: extensive experiments are conducted toevaluate the performance of the disclosed approach. Specifically, onemay first introduce the experimental setup and then the resultscorresponding to two different scenarios. Discussion on the impact ofdistinct modules is disclosed at the end.

All the data in the experiments are collected in an on-campus lab and acar over two months with 12 participants. Two devices (Tx and Rx) areput on two different sides of a round deskParticipants are invited tosit in chairs as if they were attending a meeting. During theexperiments, the participants randomly choose their seats and slightmovements are allowed. One may also conduct experiments in a car, whichis an extreme case for indoor scenario, where there is limited space aswell as strong reflection. For the car scenario, the Tx and Rx are putat the front door on the driver and passenger side respectively.

Overall Performance

FIG. 11A shows the confusion matrix of the disclosed method in the LAB,and the overall accuracy is 87.14%, with the accuracy defined as

$\begin{matrix}{{Acc} = {\frac{\# \mspace{14mu} {of}\mspace{14mu} {predicted}\mspace{14mu} {label}\mspace{14mu} {equals}\mspace{14mu} {true}\mspace{14mu} {label}}{{total}\mspace{14mu} \# \mspace{14mu} {of}\mspace{14mu} {samples}}.}} & (24)\end{matrix}$

The counting error is within 1 person for 98.6% of the testing cases.Additionally, the disclosed system can perfectly detect whether themonitoring area is occupied or not. The accuracy however, decreases withmore people present. This is as expected since the more people thereare, the more likely their breathing traces may merge together and themore likely occasional motion may happen, both leading to countingerrors. FIG. 12A shows that the disclosed testing result in the car canachieve a comparable performance with that in the LAB, whichdemonstrates the independence of the disclosed system on theenvironment.

To further evaluate the disclosed system, one may compare it with themost relevant TR-BREATH which also estimates multi-person breathingrates using commercial WiFi. TR-BREATH employs root-MUSIC for breathingrate estimation and uses the affinity propagation algorithm to estimatecrowd number. In order to make fair comparison, quasi-bilateral filteris used to filter out the outliers of original TR-BREATH estimations.The estimation accuracy of TR-BREATH in LAB and car are shown in FIG.11B and FIG. 12B respectively. As seen, TR-BREATH shows a comparableperformance in the car testing scenarios. The performance in the LABenvironments is much worse, with an overall accuracy of 70.68%. Thedisclosed approach improves the overall performance by 16.46% and 3.32%for LAB and car testing scenario respectively, attributed to its threecore techniques: adaptive SC combination, iterative dynamic programming,and trace concatenation.

FIG. 13 shows the confusion matrix of people recognition, where the PDFsof each individual are shown in FIG. 10. One can see that when thesubject has a distinct breathing rate, it corresponds to a highrecognition rate (i.e., subject 1 and subject 2). But for the case thatthe two subjects have very similar breathing rate ranges, it is hard todistinguish from each other (i.e., subject 3 and subject 4), and this isalso the reason for underestimations in the people counting system.

Performance Gain of Individual Modules: how each independent moduleimproves the performance of the disclosed system is discussed below.Apart from the confusion matrix and accuracy, here one may additionallyadopt true positive (TP) rate, which is calculated as:

$\begin{matrix}{{TP}_{i} = {\frac{\# \mspace{14mu} {of}\mspace{14mu} {samples}\mspace{14mu} {that}\mspace{14mu} {predicted}\mspace{14mu} {label}\mspace{14mu} {is}\mspace{14mu} i}{{total}\mspace{14mu} \# \mspace{14mu} {of}\mspace{14mu} {samples}\mspace{14mu} {that}\mspace{14mu} {true}\mspace{14mu} {label}\mspace{14mu} {is}\mspace{14mu} i}.}} & (25)\end{matrix}$

Impact of SNR Boosting Algorithm

Here, one may compare the SNR boosting algorithm with the commonly usedone, i.e., selecting the SCs whose maximum energy are above a certainthreshold (hereafter, it is called fixed threshold algorithm). For faircomparison, one may choose the 30% of the maximum link energy as thethreshold for both of the methods. Furthermore, the energy of each linkis normalized before link combination, thus the parameters used in laterprocess for both methods are also the same. It turns out that thedisclosed algorithm shows better performance.

Impact of IDP Estimation Algorithm

In this experiment, one may show the benefits of the IDP. One maycompare the performance with a local estimation algorithm that estimatesthe number of people based on the spectrum at current timestamp only.For fair comparison, the quasi-bilateral filter is also applied to thelocal estimation algorithm. The results shows that IDP considerablyimproves the performance for both datasets, which demonstrates the gainscontributed by leveraging time diversity in counting.

Impact of Quasi-Bilateral Filter

In this experiment, one may show the effect of the designedquasi-bilateral filter on the performance of the people counting system.FIG. 11C and FIG. 12C shows the confusion matrix of people countingsystem without filtering on datasets collected in LAB and carrespectively. By comparing the result with FIG. 11A and FIG. 12A, onecan see that the quasi-bilateral filter can improve the performance inmost cases, especially when the number of people is larger than 3 in theobservation area. The reason is that when the number of subjectsincreases, more motion interference will be introduced. Furthermore, itis more likely that different breathing traces will merge. Even thoughone may utilize the time domain as well as frequency diversity by IDP,estimation error still can occur. Quasi-bilater filter is apost-processing method that will further utilize the divisity in timedomain and thus correct the estimate outliers.

To further investigate the impact of spatial separation as well asrespiration rate difference of human subjects, one may performexperiments with 3 participants sitting with different spatialseparations, as shown in FIG. 14. Considering the volume of a humansubject, the minimum distance is set as 70 cm. The distance between Txand Rx is 3.2 m. To ensure a constant breathing rate separation duringthe experiments, each of the subjects performs controlled breathingaccording to a metronome. The breathing rate separations of [0.5, 1,1.5, 2] BPM are evaluated, respectively. FIG. 15 shows the performanceof the disclosed system, where the 4-tuple (*; *; *; *) denotes thedetection accuracy and the relative breathing rate accuracy with the 3users at location a, b, and c respectively. One can see that thebreathing rate separation has a significant impact on the performance,while the impact of the spatial separation is negligible. The detectionrate raises more than 30% when the breathing rates separation increasesfrom 0.5 BPM to 1 BPM. The system achieves 100% detection rate once thebreathing rate separation is above 1.5 BPM. Besides, as long as thebreathing rate trace has been detected, the disclosed system canaccurately estimate the breathing rate, and the breathing estimationaccuracy is above 94.7% for all of the test case.

Random body motion is one of the most challenging problems for wirelessvital sign monitoring. To combat the impact of slight motion, thepresent teaching discloses an adaptive subcarrier selection algorithm aswell as iterative dynamic programming to extract the breathing ratetrace. However, it is hard to accurately extract the breathing signalwhen continuous large motion such as walking is present due to theinherent limitation of wireless sensing. As long as there is nocontinuous large motion, which is usually the case for most wirelessmonitoring applications, the disclosed system can correctly pick up thebreathing rate traces.

There are indeed dedicated systems designed to detect the dip events ofRSS of WiFi systems when humans are walking and estimate the crowdnumber. Gait information can also be extracted from WiFi systems to dohuman recognition. However, those systems are specifically designed forthe case when the subjects need to keep walking. The disclosed systemcan perform crowd counting and recognition when the subjects are static.

Recall the experiments performed in a car to verify that the disclosedsystem is independent of the environment, during which the car engine ison but the car is not moving.

The disclosed respiration tracking method performs well with theslightly vibration introduced by the engine, however, it may not workwell in a moving car since the vibration from a running car is largeenough to overwhelm the minute vital signals. However, once the car isstatic, e.g., waiting before the traffic light, the disclosed system cantrack the respiration rate traces for crowd counting and humanrecognition. Handling car vibration during driving is a great commonchallenge faced by various sensing techniques, not only RF-based butalso vision-based approaches.

The present teaching also proposes a new breathing detection and peoplecounting engine (Breathing3). This Breathing3 engine assumes that thereare multiple antenna links, say, N1 links. For each link, it obtains atime series of channel information (TSCI) (e.g. CI may be channel stateinformation, CSI), with N2 components for each CI. There are N1 TSCI.Each TSCI can be decomposed into N2 components TSCI. Thus, there areN1×N2 component TSCI. The CI may comprise N2 components: frequencyinformation (e.g. CFR) or time information (e.g. CIR). Each CI isassociated with a time stamp. A transformation may be applied to a CIwith N2 components to convert it to a CI with N2 transformed components.For example, frequency transform may be applied to a CI with N2 timecomponent to transform it into a CI with N2 frequency component.Similarly, inverse frequency transform may be applied to a CI with N2frequency component to transform it into a CI with N2 time component. Acomponent TSCI may be formed by a particular component (e.g. the firstcomponent, or the second component, etc.) of all the CI in a TSCI. Thus,there are N1*N2 component TSCI, each associated with one of the N2components and one of the N1 links.

Breathing3 divides the time axis into overlapping time windows. For eachsliding time window and for each CIC index, it performs a time-frequencytransform (TFT, e.g. short-time Fourier Transform (STFT) or wavelet,etc.). A total of N1×N2 TFS is performed in the sliding time window, onefor each component TSCI in the corresponding sliding time window. Thesliding time window length may be adjusted over time—large when highbreathing precision is need, medium size when normal precision is ok andsmall when low precision is sufficient. Sometimes, the sounding signalmay be delayed momentarily due to network traffic/congestion causing theCI sampling/extraction (e.g. done by a wireless IC) to have irregulartime intervals (i.e. time stamps of CI are irregular). The soundingsignal may be a wireless signal from Type 1 device to Type 2 device, andmay be a training of impulses/sounding pulses sent at regular timeinterval over a period of time. Thus, sampling time correction may beapplied to ensure that the CI are sampled at regular intervals.Correction may be achieved by performing temporal interpolation at thedesired sampling time instances. Sometimes each TSCI may be sampled atdifferent sampling frequency (e.g. one sampled at 300 Hz, another one at150 Hz and still another one at 105 Hz). Sampling time correction may beapplied. All TSCI may be corrected such that all are sampled at the samesampling frequency and sampling instances.

For the sliding time window, Breathing3 forms a number ofcharacteristics intermediate time-frequency transform (CITFT), and thencombines them into a combined time-frequency transform (CTFT). The CITFTmay be computed for each link such that, for each time-frequency index(of the TFT) and for each link (one of the N1 links, between a pair ofTX and RX antennas), the CITFT may be computed as a firstcharacteristics value among the N2 TFT values of that link with thetime-frequency index and any component index. There are N1 such CITFT.For the time-frequency index (of the TFT), the CTFT is a secondcharacteristic value of the N1 CITFT. The CITFT may be computed for eachcomponent such that, for each time-frequency index (of the TFT) and foreach component (one of the N2 components of each CI), the CITFT may becomputed as a first characteristics value among the N2 TFT values ofthat link with the time-frequency index and any component index. Thereare N2 such CITFT. For the time-frequency index (of the TFT), the CTFTis a second characteristic value of the N2 CITFT. The sliding timewindow length may be adjusted over time. For example, the sliding timewindow length is large when high breathing precision is needed, has amedium size when normal precision is ok, and is small when low precisionis sufficient.

The characteristics may be the TFT value with maximum magnitude, minimummagnitude, average magnitude, a percentile magnitude, maximum phase,minimum phase, average phase, a percentile phase, mean, medium, mode,trimmed mean, weighted average, or another characteristics value. Thecharacteristics may also be the maximum magnitude, the minimummagnitude, the average magnitude, the percentile magnitude, the maximumphase, the minimum phase, the average phase, the percentile phase, themean, the medium, the mode, the trimmed mean, the weighted average, orthe another characteristics. The characteristics may be the TFT valuewith maximum magnitude, minimum magnitude, average magnitude, apercentile magnitude, maximum phase, minimum phase, average phase, apercentile phase, mean, medium, mode, trimmed mean, weighted average, oranother characteristics value after a nonlinear function is applied tothe TFT value. The nonlinear function may be logarithm, an exponentialfunction, a polynomial function or another nonlinear function. Thecharacteristics may also be the maximum magnitude, the minimummagnitude, the average magnitude, the percentile magnitude, the maximumphase, the minimum phase, the average phase, the percentile phase, themean, the medium, the mode, the trimmed mean, the weighted average, orthe another characteristics after the nonlinear function is applied tothe TFT value.

For example, each CI may be a 128-point CFR. TFT may be 512-point STFT(with frequency index, i, being from 0 to 127). The characteristics maybe the value with maximum magnitude. Suppose there are N1=2×3=6 links.Then for each STFT frequency index (e.g. i=1), there are 6 STFT values(one for each of N1 link). The CTFT at the frequency index i=1 is simplythe STFT at i=1 with maximum magnitude. If a sliding time window isshort enough (e.g. 1 second), the human breathing can be approximated asa periodic function which corresponds to a dominant peak in the CTFT andthe frequency or period associated with the dominant peak is thecorresponding breathing frequency or breathing period. Thus, inBreathing3, for each sliding time window, dominant peaks of the CTFT aredetermined. A number of breathing rates (which should be equal to thenumber of people present) are computed based on the time-frequency index(e.g. frequency of STFT) associated with dominant peaks of CTFT. Inaddition, the number of people present is deduced from the number ofbreathing rate.

Sometimes, false peaks may occur due to noise or other conditions. Tosuppress false peaks, one may note that a true peak (i.e. a truebreathing rate of a person) should NOT happen only at one time instancebut should last over time forming a “breathing trace” of the peak overtime. One can associate each sliding time window with a time t (e.g. itsstarting time). When time difference between adjacent sliding timewindow is small, the breathing of any human should not change abruptly.Thus, the breathing trace should be slowly varying (or consistent).Thus, in Breathing3, any breathing trace candidate that changes abruptly(or very fast) are suppressed, if not eliminated. For example, thebreathing trace may be obtained by dynamic programming with a costfunction that penalizes fast breath rate changes.

In Breathing3, dominant peaks of multiple sliding time window in atesting period from T1 to T2 (i.e. T1<=t<=T2, or sliding time windowswith starting time between T1 and T2) are considered together in searchof long-term “consistent” breathing. A number of breathing traces areobtained each by connecting adjacent dominant peaks (one peak from eachsliding time window) in the candidate time period (e.g. using dynamicprogramming). A dominant peak may appear in more than one breathingtraces.

A constraint may be applied that a breathing trace must begin at T1 andend at T2. As such, different testing period may be considered so thatone can detect new breathing trace (e.g. when a person just enters aroom) and disappearing breathing traces (e.g. when a person leaves theroom). The constraint may be relaxed so that a breathing trace can beginor end at any time within the testing period.

Cost function may be constructed such that abrupt-changing or fastchanging breathing rates have high cost while smooth, continuous orslow-changing breathing rates have low cost. In Breathing3, a number ofpeople may be associated with the number of breathing traces. At anytime, the instantaneous number of people may be associated with theinstantaneous number of breathing trace at that time.

In various embodiments of the present teaching, a driver authenticationor recognition may be performed based on a frequency hopping method formonitoring and recognition, e. g. as disclosed in the following clauses.

Clause 1: A method of a channel-hopping target recognition system,comprising: for each of at least one known target in a venue in arespective training time period: transmitting, by an antenna of a firstType 1 heterogeneous channel-hopping wireless device (wirelesstransmitter), a respective training channel-hopping wireless signal toat least one first Type 2 heterogeneous channel-hopping wireless device(wireless receiver) through a wireless multipath channel impacted by theknown target at the location in the venue in the training time period,wherein the wireless multipath channel comprises N1 channels, with N1greater than or equal to one, wherein the respective trainingchannel-hopping wireless signal hops among the N1 channels, obtaining,asynchronously by each of the at least one first Type 2 device based onthe training channel-hopping wireless signal, N1 time series of trainingchannel information (training CI), each time series of training CI(training TSCI) associated with one of the N1 channels of the wirelessmultipath channel between the first Type 2 device and the first Type 1device in the training time period, wherein the N1 training TSCI areasynchronous due to channel-hopping and can be merged to form a combinedtime series of training CI (combined training TSCI), and pre-processingthe N1 training TSCI; training at least one classifier for the at leastone known target based on the respective N1 training TSCI of each of theat least one known target, wherein the at least one classifier tocomprise at least one of: linear classifier, logistic regression, probitregression, Naive Bayes classifier, nonlinear classifier, quadraticclassifier, K-nearest-neighbor, decision tree, boosted tree, randomforest, learning vector quantization, linear discriminant analysis,support vector machine, neural network, perceptron, deep neural network,supervised learning, unsupervised learning, discriminative method,generative method, reinforced learning, Markov decision process,clustering, dimension reduction, K-mean, bagging, boosting, AdaBoost,stochastic gradient descent, and another classifier; and for a currenttarget in the venue in a current time period, transmitting, by anantenna of a second Type 1 heterogeneous channel-hopping wirelessdevice, a current channel-hopping wireless signal to at least one secondType 2 heterogeneous channel-hopping wireless device through thewireless multipath channel impacted by the current target in the venuein the current time period, wherein the current channel-hopping wirelesssignal hops among the N1 channels, obtaining, asynchronously by each ofthe at least one second Type 2 device based on the currentchannel-hopping wireless signal, N1 time series of current channelinformation (current TSCI), each current TSCI associated with one of theN1 channels of the wireless multipath channel between the second Type 2device and the second Type 1 device in the current time period, whereinthe N1 current TSCI are asynchronous due to channel-hopping and can bemerged to form a combined time series of current CI (combined currentTSCI), pre-processing the N1 current TSCI, and applying the at least oneclassifier to: classify at least one of: the N1 current TSCI, some ofthe N1 current TSCI, a particular current TSCI, a portion of theparticular current TSCI, a combination of the portion of the particularcurrent TSCI and another portion of an additional TSCI, a portion (intime) of the N1 current TSCI, and a combination of the portion of the N1current TSCI and another portion of an additional N1 TSCI, and associatethe current target with at least one of: one of the at least one knowntarget, a variant of the known target, an unknown target, and anothertarget, wherein each target comprises at least one of: a subject, ahuman, a baby, an elderly person, a patient, a pet, a dog, a cat, anobject, an object comprising a particular material, a machine, a device,an apparatus, a tool, a part, a component, a liquid, a fluid, amechanism, a change, a presence, an absence, a motion, a periodicmotion, a transient motion, a movement, a location, a change of thesubject, a gesture of a human, a gait of a human, a movement of a human,a change of the object, a movement of the object, a layout, anarrangement of objects, and an event, wherein a training TSCI associatedwith a first Type 2 device and a current TSCI associated with a secondType 2 device have at least one of: different starting times, differenttime durations, different stopping times, different counts of items intheir respective time series, different sampling frequencies, differentsampling periods between two consecutive items in their respective timeseries, different sounding rate, different sounding period, differentorder of channel-hopping, different duration of remaining in aparticular channel, different timing, different channels for hopping,and channel information (CI) with different features.

Clause 2: The method of the channel-hopping target recognition system ofClause 1, further comprising: aligning a first section (of a first timeduration) of a first set of TSCI and a second section (of a second timeduration) of a second set of TSCI, and determining a mapping betweenitems of the first section and items of the second section, wherein thefirst set of TSCI is associated with a subset of the N1 channels and thesecond set of TSCI is associated with the same subset of the N1channels.

Clause 3: The method of the channel-hopping target recognition system ofClause 2, wherein: the first set of TSCI is pre-processed by a firstoperation; the second set of TSCI is pre-processed by a secondoperation; and at least one of: the first operation, and the secondoperation, comprises at least one of: combining, grouping, subsampling,re-sampling, interpolation, filtering, transformation, featureextraction, and pre-processing.

Clause 4: The method of Clause 2, further comprising: mapping a firstitem of the first section to a second item of the second section,wherein at least one constraint is applied on at least one function ofat least one of: the first item of the first section of the first set ofTSCI; another item of the first set of TSCI; a time stamp of the firstitem; a time difference of the first item; a time differential of thefirst item; a neighboring time stamp of the first item; another timestamp associated with the first item; the second item of the secondsection of the second set of TSCI; another item of the second set ofTSCI; a time stamp of the second item; a time difference of the seconditem; a time differential of the second item; a neighboring time stampof the second item; and another time stamp associated with the seconditem.

Clause 5: The method of Clause 4: wherein the at least one constraint tocomprise at least one of: a difference between the time stamp of thefirst item and the time stamp of the second item is bounded by at leastone of: an adaptive upper threshold and an adaptive lower threshold, adifference between a time differential of the first item and a timedifferential of the second item is bounded by at least one of: anadaptive upper threshold and an adaptive lower threshold, and adifference between a time difference between the first item and anotheritem of the first section and a time difference between the second itemand another item of the second section is bounded by at least one of: anadaptive upper threshold and an adaptive lower threshold.

Clause 6: The method of Clause 1, further comprising: determining anactive section (of a time duration) of a set of TSCI adaptively; anddetermining a starting time and an ending time of the active section,wherein determining the active section comprises: computing a tentativesection of the set of TSCI, and determining the active section byremoving a beginning non-active portion and an ending non-active portionof the tentative section.

Clause 7: The method of Clause 6, wherein determining the sectionfurther comprises: determining the beginning portion of the tentativesection by: considering items of the tentative section with increasingtime stamp as a current item iteratively, one item at a time, computingrecursively an activity measure associated with at least one of: thecurrent item associated with a current time stamp, past items of thetentative section with time stamps not larger than the current timestamp, and future items of the tentative section with time stamps notsmaller than the current time stamp, and adding the current item to thebeginning portion of the tentative section when a first criterionassociated with the activity measure is satisfied; and determining theending portion of the tentative section by: considering items of thetentative section with decreasing time stamp as a current itemiteratively, one item at a time, iteratively computing and determiningat least one activity measure associated with at least one of: thecurrent item associated with a current time stamp, past items of thetentative section with time stamps not larger than the current timestamp, and future items of the tentative section with time stamps notsmaller than the current time stamp, and adding the current item to theending portion of the tentative section when a second criterionassociated with the at least one activity measure is satisfied.

Clause 8: The method of Clause 7: wherein at least one of the firstcriterion and the second criterion comprises at least one of: theactivity measure is smaller than an adaptive upper threshold, theactivity measure is larger than an adaptive lower threshold, theactivity measure is smaller than an adaptive upper thresholdconsecutively for at least a predetermined amount of consecutive timestamps, the activity measure is larger than an adaptive lower thresholdconsecutively for at least an additional predetermined amount ofconsecutive time stamps, the activity measure is smaller than anadaptive upper threshold consecutively for at least a predeterminedpercentage of the predetermined amount of consecutive time stamps, theactivity measure is larger than an adaptive lower thresholdconsecutively for at least another predetermined percentage of theadditional predetermined amount of consecutive time stamps, anotheractivity measure associated with another time stamp associated with thecurrent time stamp is smaller than another adaptive upper threshold andis larger than another adaptive lower threshold, at least one activitymeasure associated with at least one respective time stamp associatedwith the current time stamp is smaller than respective upper thresholdand larger than respective lower threshold, and a percentage of timestamps with associated activity measure smaller than respective upperthreshold and larger than respective lower threshold in a set of timestamps associated with the current time stamp exceeds a threshold; andwherein the activity measure associated with an item at time T1comprises at least one of: a first function of the item at time T1 andan item at time T1−D1, wherein D1 is a pre-determined positive quantity,a second function of the item at time T1 and an item at time T1+D1, athird function of the item at time T1 and an item at time T2, wherein T2is a pre-determined quantity, and a fourth function of the item at timeT1 and another item.

Clause 9: The method of Clause 8: wherein at least one of: the firstfunction, the second function, the third function, and the fourthfunction, is at least one of: a function F1(x, y, . . . ) with at leasttwo scalar arguments: x and y, a function F2(X, Y, . . . ) with at leasttwo vector arguments: X and Y, and a function F3(X1, Y1, . . . ) with atleast two arguments: X1 and Y1; wherein the function F1 is a function ofat least one of the following: x, y, (x−y), (y−x), abs(x−y),x{circumflex over ( )}a1, y{circumflex over ( )}b1, abs(x{circumflexover ( )}a1−y{circumflex over ( )}b1), (x−y){circumflex over ( )}a1,(x/y), (x+a1)/(y+b1), (x{circumflex over ( )}a1/y{circumflex over( )}b1), and ((x/y){circumflex over ( )}a1−b1), wherein a1 and b1 arepredetermined quantities; wherein both X and Y are n-tuples such thatX=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2, . . . , y_n); the function F2is a function of at least one of the following: x_i, y_i, (x_i−y_i),(y_i−x_i), abs(x_i−y_i), x_i{circumflex over ( )}a2, y_i{circumflex over( )}b2, abs(x_i{circumflex over ( )}a2−y_i{circumflex over ( )}b2),(x_i−y_i){circumflex over ( )}a2, (x_i/y_i), (x_i+a2)/(y_i+b2),(x_i{circumflex over ( )}a2/y_i{circumflex over ( )}b2), and((x_i/y_i){circumflex over ( )}a2−b2); wherein i, ranging from 1 to n,is a component index of the n-tuples X and Y; wherein both X1 and Y1 aren-tuples comprising N components such that X1=(x1_1, x1_2, . . . , x1_N)and Y1=(y1_1, y1_2, . . . , y1_N); wherein the function F3 comprises acomponent-by-component summation of another function of at least one ofthe following: x1_j, y1_j, (x1_j−y1_j), (y1_j−x1_j), abs(x1_j−y1_j),x1_j{circumflex over ( )}a3, y1_j{circumflex over ( )}b3,abs(x1_j{circumflex over ( )}a3−y1_j{circumflex over ( )}b3),(x1_j−y1_j){circumflex over ( )}a3, (x1_j/y1_j), (x1_j+a3)/(y1_j+b3),(x1_j{circumflex over ( )}a3/y1_j{circumflex over ( )}b3), and((x1_j/y1_j){circumflex over ( )}a3−b3); and wherein j, ranging from 1to N, is a component index of the n-tuples X1 and Y1.

Clause 10: The method of Clause 2, further comprising: computing themapping using dynamic time warping (DTW), wherein the DTW comprises aconstraint on at least one of: the mapping, the items of the first setof TSCI, the items of the second set of TSCI, the first time duration,the second time duration, the first section, and the second section.

Clause 11: The method of Clause 1, further comprising: aligning a firstsection (of a first time duration) of a first set of TSCI and a secondsection (of a second time duration) of a second set of TSCI; computing amap comprising a plurality of links between first items of the firstsection and second items of the second section, wherein each of theplurality of links associates a first item with a first time stamp witha second item with a second time stamp; computing a mismatch costbetween the aligned first section and the aligned second section;applying the at least one classifier based on the mismatch cost, whereinthe mismatch cost comprises at least one of: an inner product, aninner-product-like quantity, a quantity based on correlation, a quantitybased on covariance, a discriminating score, a distance, a Euclideandistance, an absolute distance, an L_1 distance, an L_2 distance, an L_kdistance, a weighted distance, a distance-like quantity and anothersimilarity value, between the first vector and the second vector, and afunction of: an item-wise cost between a first item of the first sectionof the first CI time series and a second item of the second section ofthe second CI time series associated with the first item by a link ofthe map, and a link-wise cost associated with the link of the map,wherein the aligned first section and the aligned second section arerepresented respectively as a first vector and a second vector that havesame vector length, and wherein the mismatch cost is normalized by thevector length.

Clause 12: The method of Clause 11, further comprising: applying the atleast one classifier to a plurality of first sections of the first setof TSCI and a plurality of respective second sections of the second setof TSCI; obtaining at least one tentative classification result, eachtentative classification result being associated with a respective firstsection and a respective second section; and associating the currenttarget with at least one of: the known target, the unknown target andthe another target, based on a largest number of the at least onetentative classification result associated with at least one of: theknown target, the unknown target and the another target.

Clause 13: The method of Clause 1, further comprising: training aprojection for each CI using a dimension reduction method based on theN1 training TSCI associated with each known target, wherein thedimension reduction method comprises at least one of: principalcomponent analysis (PCA), PCA with different kernel, independentcomponent analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, and deep neuralnetwork; applying the projection to each respective CI; training the atleast one classifier based on the projection and the N1 training TSCIassociated with each known target; classifying, by the at least oneclassifier, the N1 current TSCI based on the projection; and associatingthe N1 current TSCI with at least one of: the known target, the variantof the known target, the unknown target, and the another target.

Clause 14: The method of Clause 13, further comprising: a re-trainingcomprising at least one of: re-training the projection using at leastone of: the dimension reduction method, and an additional dimensionreduction method, based on at least one of: the at least one classifierbefore the re-training, the projection before the re-training, the N1training TSCI associated with each known target, the N1 current TSCI,and additional training TSCI, wherein the additional dimension reductionmethod comprises at least one of: a simplified dimension reductionmethod, principal component analysis (PCA), PCA with different kernels,independent component analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, and deep neuralnetwork, and re-training the at least one classifier based on at leastone of: the re-trained projection, the N1 training TSCI associated witheach known target, the result of the application of the at least oneclassifier, and the N1 current TSCI; and classifying N1 next TSCIassociated with the N1 channels based on at least one of: the re-trainedprojection, and the at least one re-trained classifier.

Clause 15: The method of Clause 1, further comprising: training aprojection for each of a set of combined training CI using a dimensionreduction method based on the N1 training TSCI associated with eachknown target, wherein each combined CI of a known target is associatedwith a particular time stamp, and is a combination of N1 CI with a CIassociated with each of the N1 channels, each CI being computed from atime window of the corresponding training TSCI of the known targetaround to the particular time stamp, wherein the dimension reductionmethod comprises at least one of: principal component analysis (PCA),PCA with different kernel, independent component analysis (ICA), Fisherlinear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, and deep neural network; applying the projection to eachcombined CI; training the at least one classifier based on theprojection, the set of combined training CI of each known target, andthe N1 training TSCI associated with each known target; and classifying,by the at least one classifier, the N1 current TSCI based on theprojection.

Clause 16: The method of Clause 15, further comprising: a retrainingcomprising at least one of: re-training the projection using at leastone of: the dimension reduction method, and an additional dimensionreduction method, based on at least one of: the projection before there-training, the set of combined training CI of each known target, theN1 training TSCI associated with each known target, the combined currentTSCI, the N1 current TSCI, and additional training TSCI, wherein theadditional dimension reduction method comprises at least one of: asimplified dimension reduction method, principal component analysis(PCA), PCA with different kernels, independent component analysis (ICA),Fisher linear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, and deep neural network, and re-training the at least oneclassifier based on at least one of: the re-trained projection, the setof combined training CI of each known target, the N1 training TSCIassociated with each known target, the combined current TSCI, the N1current TSCI, and the additional training TSCI; and classifying N1 nextTSCI associated with the N1 channels based on at least one of: there-trained projection, and the at least one re-trained classifier.

Clause 17: The method of Clause 1, further comprising: for each of atleast one first section (of a first time duration) of the combinedcurrent TSCI: for each of the at least one known target: determining arespective second section (of a respective second time duration) of arespective representative combined training TSCI of the respectivetarget, aligning the first section and the respective second section,and computing a mismatch cost between the aligned first section and thealigned respective second section, applying the at least one classifier,and obtaining a tentative classification of the first section based onthe mismatch costs; obtaining a classification of the at least one firstsection result based on at least one of: the at least one tentativeclassification of each first section, the mismatch cost of each knowntarget for each first section, the combined current TSCI, the combinedtraining TSCI, and associating the at least one first section with atleast one of: a known target, an unknown target and another target.

Clause 18: The method of Clause 17, further comprising: computing howmany times each known target achieves a smallest mismatch cost; andassociating the at least one first section with at least one of: a knowntarget that achieves the smallest mismatch cost for most times, a knowntarget that achieves a smallest overall mismatch cost, wherein anoverall mismatch cost is a weighted average of at least one mismatchcost associated with the at least one first section, a known target thatachieves a smallest cost based on another overall cost, and an unknowntarget, wherein the at least one first section is associated with theunknown target in at least one of the following situations: no targetachieves a mismatch cost lower than a first threshold T1 in a sufficientpercentage of the at least one first section, and no target achieves anoverall mismatch cost lower than a second threshold T2.

Clause 19: The method of Clause 17; wherein the representative combinedtraining CI time series associated with the known target is obtainedbased on the combined training TSCI associated with the known target andadditional combined training TSCI associated with the known target.

Clause 20: The method of Clause 17: wherein the representative combinedtraining TSCI associated with the known target is a particular combinedtraining TSCI among the set of all possible combined training TSCI thatminimizes an aggregate mismatch with respect to a training set of TSCI;wherein the training set of TSCI comprises the combined training TSCIassociated with the known target and additional combined training TSCIassociated with the known target; wherein the aggregate mismatch of aparticular TSCI with respect to the training set is a function of atleast one mismatch cost between the particular TSCI and each TSCI of thetraining set of TSCI, wherein both are aligned before the mismatch costis computed; wherein the function comprises at least one of: average,weighed average, mean, trimmed mean, median, mode, arithmetic mean,geometric mean, harmonic mean, truncated mean, generalized mean, powermean, f-mean, interquartile mean, and another mean.

Clause 21: The method of Clause 20: wherein the particular combinedtraining TSCI is in the training set of TSCI.

Clause 22: The method of Clause 20: wherein each TSCI is associated witha time duration; wherein a set of candidate time durations of therespective combined training TSCI are determined; wherein, for eachcandidate time duration, a respective optimal combined training TSCIwith the candidate time duration minimizing the aggregate mismatch withrespect to the training set of TSCI is computed; wherein therepresentative combined training TSCI is the particular optimal combinedtraining TSCI with the particular time duration among the set ofcandidate time duration that minimizes the aggregate mismatch.

Clause 23: The method of Clause 22: wherein fast computation is appliedin the search of the particular time duration, and the particularoptimal combined training TSCI with the particular time duration.

Clause 24: The method of Clause 1, further comprising: determining arespective time window of a respective training TSCI for the training ofa classifier for the at least one known target.

Clause 25: The method of Clause 1, further comprising: re-training aclassifier for the at least one known target adaptively based on atleast one of: a current TSCI, the combined current TSCI, a previouscurrent TSCI and a pervious combined current TSCI.

Clause 26: The method of clause 25, further comprising: determining atime window associated with at least one of: the current TSCI, thecombined current TSCI, the previous current TSCI and the previouscombined current TSCI, for the re-training of the classifier for the atleast one known target.

Clause 27: The method of Clause 1: wherein the venue is a vehicle andthe current target is a passenger in the vehicle; wherein the at leastone known target comprises a number of known passengers of the vehicle;wherein the at least one classifier to associate the current target witha known passenger.

Clause 28: The method of Clause 27: wherein at least two known targetscomprise a particular known passenger with different postures in thevehicle; wherein the different postures comprise a body part of theparticular known passenger at least one of: different positions,different orientation and different shape; wherein the at least oneclassifier to associate the current target with a passenger posture.

Clause 29: The method of Clause 27: wherein at least two known targetscomprise a particular known passenger doing different movement in thevehicle; wherein the different movement comprise at least one of: adriver movement, driving straight, turning left, turning right, steppingon accelerator, stepping on brake, changing gear, adjusting turningsignal, adjusting windshield wiper setting, adjusting visor, adjustingdashboard switches, adjusting mirror, adjusting window, reaching to theback, reaching to the side, reaching to center console, putting on acap, adjusting seat, driving attentively, driving in a sleepy manner, apassenger movement, sitting quietly looking forward, looking sideway,sleeping, talking, checking phone, working on a laptop, stretching,adjusting window, and another movement; wherein the at least oneclassifier to associate the current target with a particular passengermovement.

Clause 30: The method of Clause 27: wherein at least two known targetscomprise a particular known passenger in different states; wherein thestates comprise at least one of: an emotional state, normal, depressed,excited, a physiological state, sleepy, alert, relax, tense; wherein theat least one classifier to associate the current target with aparticular passenger state.

Clause 31: The method of Clause 27: wherein at least two known targetscomprise a particular known passenger sitting at different locations inthe vehicle; wherein the at least one classifier to associate thecurrent target with a particular location in the vehicle.

Clause 32: The method of Clause 1: wherein the venue is a vehicle andthe current target comprises two passengers in the vehicle; wherein atleast two known targets are “single” targets each comprising a singleknown passenger of the vehicles; wherein the at least one classifier toassociate the current target with two of the at least two singletargets.

Clause 33: The method of Clause 32: wherein at least two known targetsare single targets each comprising a particular known passenger with atleast one of: different postures, different movement, different state,and another difference; wherein the at least one classifier to associatethe current target with at least one of: a passenger posture, apassenger movement, a passenger state and another characteristics.

Clause 34: The method of Clause 1: wherein the venue is a vehicle andthe current target comprises two passengers in the vehicle; wherein atleast two known targets are “double” targets each comprising two knownpassengers of the vehicles; wherein the at least one classifier toassociate the current target with one of the double targets.

Clause 35: The method of Clause 34: wherein at least two known doubletargets comprise a particular known passenger with at least one of:different postures, different movement, different state, and anotherdifference.

Clause 36: The method of Clause 1: wherein the venue is a vehicle andthe current target comprises a first number of passengers in thevehicle; wherein at least two known targets are “multi-targets” eachcomprising a respective second number of known passengers of thevehicle; wherein the second number is a non-negative integer not lessthan 0 and not greater than the first number; wherein the at least oneclassifier to associate the current target with at least onemulti-target.

Clause 37: The method of Clause 36: wherein at least two of the knownmulti-targets comprise a particular known passenger with at least oneof: different postures, different movement, different state, and anotherdifference.

Clause 38: The method of Clause 1: wherein the venue is a vehicle andthe current target comprises an unknown number of passengers in thevehicle; wherein at least two known targets are “multi-targets” eachcomprising a number of known passengers of the vehicles; wherein the atleast one classifier to associate the current target with at least onemulti-target, and an estimate of the unknown number based on the numberof known passengers associated with each of the at least onemulti-target.

Clause 39: The method of Clause 1: wherein the venue is a vehicle andthe current target comprises an unknown number of passengers in thevehicle; wherein at least two known targets are “multi-targets” eachcomprising a number of known passengers of the vehicles; wherein the atleast one classifier to associate the current target with an estimate ofthe unknown number.

Clause 40: The method of Clause 1: wherein the venue is a vehicle andthe current target is a passenger at a particular position in thevehicle; wherein the at least one known target comprises a number ofknown passengers of the vehicle at a number of known positions in thevehicle; wherein the at least one classifier to associate the currenttarget with the particular position of the vehicle.

Clause 41: The method of Clause 27: wherein the passenger sits at thedriver seat of the vehicle.

Clause 42: The method of Clause 27: wherein the current motion happensbefore the vehicle is started comprising the passenger opening thedriver-side front door, entering the vehicle and sitting at the driverseat of the vehicle.

Clause 43: The method of Clause 27: wherein, after the vehicle is turnedoff, the passenger sitting at the driver seat of the vehicle opens thedriver-side front door, and exits the vehicle.

Clause 44: The method of Clause 27: wherein the current motion happensbefore the vehicle is started comprising the passenger opening thepassenger-side front door, entering the vehicle and sitting at the seatnext to the driver's seat of the vehicle.

Clause 45: The method of Clause 27: wherein the passenger sitting at theseat of the vehicle next to the driver seat opens the passenger-sidefront door and exits the vehicle.

Clause 46: The method of Clause 27: wherein the current motion happensbefore the vehicle is started comprising the passenger opening a backdoor, entering the vehicle and sitting at a seat in the second row ofthe vehicle.

Clause 47: The method of Clause 27: wherein the current motion happensbefore the vehicle is started comprising the passenger opening a backdoor, entering the vehicle and sitting at a position behind the secondrow of the vehicle.

Clause 48: The method of Clause 27: wherein the current motion happensbefore the vehicle is started comprising the opening of a front door,the passenger entering the vehicle and sitting at a position behind thefirst row of the vehicle.

Clause 49: The method of Clause 1: wherein the venue is a vehicle;wherein at least one classifier is trained or re-trained for: a numberof known targets each being the vehicle being one of a number of knowncandidate vehicles, a number of known targets each being the vehicle ina known state, a target being the vehicle with all doors and windowsclosed, a number of known targets each being the vehicle with all doorsclosed and one window opened to a known degree, a number of knowntargets each being the vehicle with all doors closed and a number ofwindows opened to respective known degrees, a number of known targetseach being the vehicle with driver door opened to a known degree and theother doors closed, a number of known targets each being the vehiclewith front passenger door opened to a known degree and the other doorsclosed, a number of known targets each being the vehicle with a backdoor opened to a known degree and the other doors closed, a number ofknown targets each being the vehicle with a number of doors opened torespective known degrees and the other doors closed, a number of knowntargets each being the vehicle with tail gate opened and the other doorsclosed, a number of known targets each being the vehicle with seats atrespective known positions, known angles and known settings, a number ofknown targets each being the vehicle with accessories at respectivelyknown settings, a number of known targets each being the vehicle with anumber of seats occupied by passengers, a number of known targets eachbeing the vehicle with a number of car-seats installed, and a number ofknown targets each being the vehicle with a number of child boosterseats installed.

Clause 50: The method of Clause 1: wherein the venue is a vehicle;wherein at least one classifier is trained or re-trained based on atleast one of: TSCI of the vehicle obtained recently when the vehicle isnot in a trip, TSCI of the vehicle obtained in a current trip, TSCI ofthe vehicle obtained in a recent trip, TSCI of the vehicle obtained inan immediate past trip, TSCI of the vehicle obtained in a recent tripwhen the vehicle is a particular known vehicle, TSCI of the vehicleobtained in a recent trip when the vehicle is in a particular state,TSCI of the vehicle obtained in a recent trip when a particular knowndriver is driving, TSCI of the vehicle obtained in a recent trip when aparticular known passenger is riding in the vehicle, TSCI of the vehicleobtained in a recent trip when a particular known passenger is in atleast one of: a particular position, a particular posture, a particularmovement, and a particular state, in the vehicle. TSCI of the vehicleobtained in a recent trip when a particular group of known passengersare riding in the vehicle, and TSCI of the vehicle obtained in a recenttrip when a particular group of seats are occupied in the vehicle.

Clause 51: The method of Clause 27: wherein the at least one classifieris applied within a time window around a moment when the driver door isopened and closed.

Clause 52: The method of Clause 27: wherein the at least one classifieris applied within a time window around a moment when the vehicle engineis started.

Clause 53: The method of Clause 27: wherein the at least one classifieris applied within a time window around a moment when the vehicle engineis stopped.

Clause 54: The method of Clause 27: wherein the at least one classifieris applied while the vehicle is in motion.

Clause 55: The method of Clause 27: wherein the at least one classifieris applied when a button of the vehicle is pressed.

Clause 56: The method of Clause 27: wherein the at least one classifieris applied upon receiving a control signal.

Clause 57: The method of Clause 27, further comprising: adjusting atleast one component of the vehicle based on the known passenger.

Clause 58: The method of Clause 27, further comprising: adjusting atleast one component of the vehicle based on a preference or a previoussetting or a condition or a characteristics of the known passenger.

Clause 59: The method of Clause 27, further comprising: adjusting adriver seat setting of the vehicle based on a preference or a previoussetting or a condition or a characteristics associated with the knownpassenger.

Clause 60: The method of Clause 27, further comprising: adjusting a seatsetting of the vehicle based on a preference or a previous setting or acondition or a characteristics of the known passenger.

Clause 61: The method of Clause 27, further comprising: adjusting adriver seat setting of the vehicle based on a preference or a previoussetting or a condition or a characteristics of a known driver, whereinthe recognized passenger is the known driver.

Clause 62: The method of Clause 27, further comprising: adjusting anentertainment system setting of the vehicle based on a preference or aprevious setting or a condition or a characteristics of the knownpassenger.

Clause 63: The method of Clause 27, further comprising: adjusting an airconditioning system setting of the vehicle based on a preference or aprevious setting or a condition or a characteristics of the knownpassenger.

Clause 64: The method of Clause 27, further comprising: adjusting avehicle subsystem setting of the vehicle based on a preference or aprevious setting or a condition or a characteristics of the knownpassenger.

Clause 65: The method of Clause 32 or Clause 33 or Clause 34 or Clause35, further comprising: adjusting a vehicle subsystem setting of thevehicle based on a preference or a previous setting or a condition or acharacteristics of two passenger.

Clause 66: The method of Clause 36 or Clause 37, further comprising:adjusting a vehicle subsystem setting of the vehicle based on apreference or a previous setting or a condition or a characteristics ofthe first number of passengers.

Clause 67: The method of Clause 1: wherein N1 is equal to 1 such thatthere is no channel-hopping over time.

Clause 68: The method of Clause 1: wherein N1 is equal to 1 at least fora period of time such that there is no channel-hopping during the periodof time.

In various embodiments of the present teaching, monitoring a rhythmicmotion like breathing may be performed according to the followingclauses.

Clause A1: A method/software/apparatus/system of a rhythmic motionmonitoring system, comprising: obtaining a number, N1, of time series ofchannel information (CI) of a wireless multipath channel of a venueusing a processor, a memory communicatively coupled with the processorand a set of instructions stored in the memory, wherein the N1 timeseries of CI (TSCI) are extracted from a wireless signal transmittedbetween a Type 1 heterogeneous wireless device (wireless transmitter)with at least one antenna and a Type 2 heterogeneous wireless device(wireless receiver) with at least one antenna in the venue through thewireless multipath channel, wherein each of the N1 TSCI is associatedwith an antenna of the Type 1 device and an antenna of the Type 2device, wherein each CI has N2 components such that each TSCI can isdecomposed into N2 time series of CIC (TSCIC), each TSCIC comprising aparticular component of all the CI of the TSCI, wherein the wirelessmultipath channel is impacted by a rhythmic motion of an object in thevenue; and monitoring the rhythmic motion of the object based on theN1*N2 TSCIC, and triggering a response action based on the monitoring ofthe rhythmic motion of the object.

Clause A2: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A1: wherein each CI to comprise at least one of: N2frequency-domain components, and N2 time-domain components, wherein eachCI is associated with a time stamp.

Clause A3: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A2: wherein a frequency transform is applied to each CIwith N2 time-domain components to transform it to another CI with N2frequency-domain components.

Clause A4: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A2: wherein an inverse frequency transform is appliedto each CI with N2 frequency-domain components to transform it toanother CI with N2 time-domain components.

Clause A5: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A2, further comprising: re-sampling the CI to ensurethat the CI are uniformly sampled (time stamp evenly spaced in time).

Clause A6: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A1, further comprising: segmenting each TSCI intooverlapping segments of CI, each overlapping segment comprising CI withtime stamp in a sliding time window, wherein each overlapping segment isassociated with a time stamp; monitoring the rhythmic motion of theobject in each overlapping segment.

Clause A7: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A1, further comprising: segmenting each TSCIC intooverlapping segments of CIC, each segment comprising CIC with time stampin a sliding time window; monitoring the rhythmic motion of the objectbased on the N1*N2 TSCIC in each overlapping segment.

Clause A8: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A7, further comprising: computing a short-timetransformation (STT) of each of the N1*N2 TSCIC in each overlappingsegment, wherein the STT to comprise at least one of: autocorrelationfunction, cross-correlation function, auto-covariance function,cross-covariance function, linear transform applied to a limited timeperiod, orthogonal transform, inverse transform, power-of-2 transform,sparse transform, graph-based transform, graph signal processing, fasttransform, a transform combined with zero padding, Fourier transform,discrete Fourier transform, discrete time Fourier transform, discreteFourier transform, wavelet transform, Laplace transform. Hilberttransform, Hadamard transform, slant transform, trigonometric transform,sine transform, cosine transform, another transform, spectrum, spectrummagnitude, spectrum phase, spectrum warping, polynomial warping, squarewarping, square-root warping, harmonic product spectrum (HPS),sub-harmonic summation (SHS), subharmonic-to-harmonic ratio (SHR),harmonic sieve (HS), average magnitude difference function (AMDF),average squared difference function (ASDF), cepstrum (CEP), averagepeak-to-valley distance (APVD), sawtooth-waveform inspired pitchestimator (SWIPE), harmonics weighting, harmonics limitation, harmonicdiscarding, frequency scale warping, frequency emphasis, time scalewarping, temporal emphasis, windowing, preprocessing, postprocessing, anoperation, a function of operands, filtering, linear filtering,nonlinear filtering, lowpass filtering, bandpass filtering, highpassfiltering, median filtering, rank filtering, quartile filtering,percentile filtering, mode filtering, finite impulse response (FIR)filtering, infinite impulse response (IIR) filtering, moving average(MA) filtering, autoregressive (AR) filtering, autoregressive movingaveraging (ARMA) filtering, selective filtering, adaptive filtering,folding, grouping, energy computation, interpolation, decimation,subsampling, upsampling, resampling, cyclic padding, padding, zeropadding, sorting, thresholding, soft thresholding, hard thresholding,clipping, soft clipping, time correction, time base correction, phasecorrection, magnitude correction, phase cleaning, magnitude cleaning,matched filtering, Kalman filtering, particle filter, enhancement,restoration, denoising, smoothing, signal conditioning, intrapolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, representing, merging, combining, splitting, scrambling, errorprotection, forward error correction, spectral analysis, lineartransform, nonlinear transform, frequency transform, inverse frequencytransform, Fourier transform, wavelet transform, Laplace transform,Hilbert transform, Hadamard transform, trigonometric transform, sinetransform, cosine transform, power-of-2 transform, sparse transform,graph-based transform, graph signal processing, fast transform, atransform combined with zero padding, feature extraction, decomposition,projection, orthogonal projection, non-orthogonal projection,over-complete projection, eigen-decomposition, singular valuedecomposition (SVD), principle component analysis (PCA), independentcomponent analysis (ICA), first derivative, second order derivative,high order derivative, convolution, multiplication, division, addition,subtraction, integration, maximization, minimization, localmaximization, local minimization, optimization of a cost function,vector addition, vector subtraction, vector multiplication, vectordivision, inverse, norm, distance, similarity score computation, neuralnetwork, recognition, labeling, training, clustering, machine learning,supervised learning, unsupervised learning, semi-supervised learning,comparison with another TSCI, quantization, vector quantization,matching pursuit, compression, encryption, coding, storing,transmitting, normalization, temporal normalization, frequency domainnormalization, classification, clustering, labeling, tagging, learning,detection, estimation, learning network, mapping, remapping, expansion,storing, retrieving, transmitting, receiving, representing, merging,combining, splitting, tracking, monitoring, time varying processing,conditioning averaging, weighted averaging, arithmetic mean, geometricmean, harmonic mean, weighted mean, trimmed mean, median, mode,averaging over selected frequency, averaging over antenna links, logicaloperation, permutation, combination, sorting, AND, OR, XOR, union,intersection, another operation, and another transformation; monitoringthe rhythmic motion of the object based on STT of the N1*N2 TSCIC ineach overlapping segment.

Clause A9: The method/apparatus/system of the rhythmic motion monitoringsystem of clause A7, further comprising: adjusting the length of theoverlapping segments over time based on a precision requirement of thefrequency of the rhythmic motion.

Clause A10: The method/apparatus/system of the rhythmic motionmonitoring system of clause A7, further comprising: adjusting thesampling time of the CI before the short-time frequency transform suchthat the CI and CIC are uniformly sampled such that the time stamps ofthe CI in each TSCI and the CIC in each TSCIC are uniformly spaced intime.

Clause A11: The method/apparatus/system of the rhythmic motionmonitoring system of clause A10: wherein the sampling time of a CI isadjusted by interpolating a respective re-sampled CI at a desirable timestamp based on the CI and neighboring CI.

Clause A12: The method/apparatus/system of the rhythmic motionmonitoring system of clause A10: wherein the sampling time of a CIC isadjusted by interpolating a respective re-sampled CIC at a desirabletime stamp based on the CIC and neighboring CIC.

Clause A13: The method/apparatus/system of the rhythmic motionmonitoring system of clause A1, further comprising: monitoring therhythmic motion of the object by computing at least one analyticsassociated with the rhythmic motion of the object based on at least oneof: the N1 TSCI and the N1*N2 TSCIC, wherein the at least one analyticsto comprise at least one of: a frequency, a period, a periodicityparameter, a rhythm, an intensity, a phase, a musical note, a pitch, anexpression with a rhythm, a musical rhythm, a punctuated rhythm, a beat,a pace, a walking rhythm, an exercise rhythm, a sports rhythm, avibration parameter, an oscillation parameter, a pulsation parameter, arelaxing parameter, a motor parameter, an impulsive rhythm, an explosiverhythm, a dominant rhythm, a foreground rhythm, a background rhythm, arandom rhythm, a chaotic rhythm, a stochastic rhythm, a breathing rate,a breathing period, a heart rate, a vital sign, a change of thebreathing rate, a change of the heart rate, a change of the vital sign,a count, a count associated the rhythmic motion of the object, a countof sources of rhythmic motion, a count of the object and other objects,a count of people, a change in the count, a tag, a tag associated withthe rhythmic motion, a tag associated with the object, an activityassociated with the rhythmic motion, a change in the tag, a location, alocation associated with the rhythmic motion, a location associated withthe object, a change in the location, a time, a timing, a startingtime/timing, a stopping time/timing, a pausing time/timing, aninterruption time/timing, a repeating/hourly/daily/weekly/monthly/yearlyactivity pattern, a repeating timing, movement pattern, a repeatingtrend, a daily activity associated with the rhythmic motion, a dailyactivity, a spontaneous activity, a variation, a variation of thefrequency, a variation of pitch, prosody, a variation of the period, avariation of rhythm, a variation of intensity, a variation of phase, avariation of parameter, a relationship between two rhythms, acause-and-effect between two rhythms, and another rhythm parameter.

Clause A14: The method/apparatus/system of the rhythmic motionmonitoring system of clause A1, further comprising: monitoring therhythmic motion of the object by computing at least one analyticsassociated with the rhythmic motion of the object based on a processingof at least one of: the N1 TSCI and the N1*N2 TSCIC, wherein theprocessing to comprise at least one of: preprocessing, postprocessing,an operation, a function of operands, filtering, linear filtering,nonlinear filtering, lowpass filtering, bandpass filtering, highpassfiltering, median filtering, rank filtering, quartile filtering,percentile filtering, mode filtering, finite impulse response (FIR)filtering, infinite impulse response (IIR) filtering, moving average(MA) filtering, autoregressive (AR) filtering, autoregressive movingaveraging (ARMA) filtering, selective filtering, adaptive filtering,folding, grouping, energy computation, interpolation, decimation,subsampling, upsampling, resampling, cyclic padding, padding, zeropadding, sorting, thresholding, soft thresholding, hard thresholding,clipping, soft clipping, time correction, time base correction, phasecorrection, magnitude correction, phase cleaning, magnitude cleaning,matched filtering, Kalman filtering, particle filter, enhancement,restoration, denoising, smoothing, signal conditioning, intrapolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, representing, merging, combining, splitting, scrambling, errorprotection, forward error correction, spectral analysis, lineartransform, nonlinear transform, frequency transform, inverse frequencytransform, Fourier transform, wavelet transform, Laplace transform,Hilbert transform, Hadamard transform, trigonometric transform, sinetransform, cosine transform, power-of-2 transform, sparse transform,graph-based transform, graph signal processing, fast transform, atransform combined with zero padding, feature extraction, decomposition,projection, orthogonal projection, non-orthogonal projection,over-complete projection, eigen-decomposition, singular valuedecomposition (SVD), principle component analysis (PCA), independentcomponent analysis (ICA), first derivative, second order derivative,high order derivative, convolution, multiplication, division, addition,subtraction, integration, maximization, minimization, localmaximization, local minimization, optimization of a cost function,vector addition, vector subtraction, vector multiplication, vectordivision, inverse, norm, distance, similarity score computation, neuralnetwork, recognition, labeling, training, clustering, machine learning,supervised learning, unsupervised learning, semi-supervised learning,comparison with another TSCI, quantization, vector quantization,matching pursuit, compression, encryption, coding, storing,transmitting, normalization, temporal normalization, frequency domainnormalization, classification, clustering, labeling, tagging, learning,detection, estimation, learning network, mapping, remapping, expansion,storing, retrieving, transmitting, receiving, representing, merging,combining, splitting, tracking, monitoring, time varying processing,conditioning averaging, weighted averaging, arithmetic mean, geometricmean, harmonic mean, weighted mean, trimmed mean, median, mode,averaging over selected frequency, averaging over antenna links, logicaloperation, permutation, combination, sorting, AND, OR, XOR, union,intersection, doing nothing, and another operation.

Clause A15: The method/apparatus/system of the rhythmic motionmonitoring system of clause A8, further comprising: monitoring therhythmic motion of the object by computing at least one analyticsassociated with the rhythmic motion of the object based on STT of theN1*N2 TSCIC, wherein the at least one analytics to comprise at least oneof: a frequency, a period, a periodicity parameter, a rhythm, anintensity, a phase, a musical note, a pitch, an expression with arhythm, a musical rhythm, a punctuated rhythm, a beat, a pace, a walkingrhythm, an exercise rhythm, a sports rhythm, a vibration parameter, anoscillation parameter, a pulsation parameter, a relaxing parameter, amotor parameter, an impulsive rhythm, an explosive rhythm, a dominantrhythm, a foreground rhythm, a background rhythm, a random rhythm, achaotic rhythm, a stochastic rhythm, a breathing rate, a breathingperiod, a heart rate, a vital sign, a change of the breathing rate, achange of the heart rate, a change of the vital sign, a count, a countassociated the rhythmic motion of the object, a count of sources ofrhythmic motion, a count of the object and other objects, a count ofpeople, a charge in the count, a tag, a tag associated with the rhythmicmotion, a tag associated with the object, an activity associated withthe rhythmic motion, a change in the tag, a location, a locationassociated with the rhythmic motion, a location associated with theobject, a change in the location, a time, a timing, a startingtime/timing, a stopping time/timing, a pausing time/timing, aninterruption time/timing, a repeating/hourly/daily/weekly/monthly/yearlyactivity pattern, a repeating timing, movement pattern, a repeatingtrend, a daily activity associated with the rhythmic motion, a dailyactivity, a spontaneous activity, a variation, a variation of thefrequency, a variation of pitch, prosody, a variation of the period, avariation of rhythm, a variation of intensity, a variation of phase, avariation of parameter, a relationship between two rhythms, acause-and-effect between two rhythms, and another rhythm parameter.

Clause A16: The method/apparatus/system of the rhythmic motionmonitoring system of clause A15, further comprising: computing acombined analytics as a function of N1*N2 analytics, each analyticsassociated with a respective TSCIC, wherein the function to comprise atleast one of: scalar function, vector function, discrete function,continuous function, polynomial function, characteristics, feature,magnitude, phase, exponential function, logarithmic function,trigonometric function, transcendental function, logical function,linear function, algebraic function, nonlinear function, piecewiselinear function, real function, complex function, vector-valuedfunction, inverse function, derivative of function, integration offunction, circular function, function of another function, one-to-onefunction, one-to-many function, many-to-one function, many-to-manyfunction, zero crossing, absolute function, indicator function, mean,mode, median, range, statistics, variance, arithmetic mean, geometricmean, harmonic mean, weighted mean, trimmed mean, conditional mean,percentile, maximum, minimum, square, cube, root, power, sine, cosine,tangent, cotangent, secant, cosecant, elliptical function, parabolicfunction, hyperbolic function, game function, zeta function, absolutevalue, thresholding, limiting function, floor function, roundingfunction, sign function, quantization, piecewise constant function,composite function, function of function, time function processed withan operation (e.g. filtering), probabilistic function, stochasticfunction, random function, ergodic function, stationary function,deterministic function, periodic function, transformation, frequencytransform, inverse frequency transform, discrete time transform, Laplacetransform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, sparse transform, projection, decomposition, principlecomponent analysis (PCA), independent component analysis (ICA), neuralnetwork, feature extraction, moving function, function of moving windowof neighboring items of time series, filtering function, convolution,mean function, variance function, statistical function, short-timetransform, discrete transform, discrete Fourier transform, discretecosine transform, discrete sine transform, Hadamard transform,eigen-decomposition, eigenvalue, singular value decomposition (SVD),singular value, orthogonal decomposition, matching pursuit, sparsetransform, sparse approximation, any decomposition, graph-basedprocessing, graph-based transform, graph signal processing,classification, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, smoothing, medianfiltering, mode filtering, sampling, random sampling, resamplingfunction, downsampling, upsampling, interpolation, extrapolation,importance sampling, Monte Carlo sampling, compressive sensing,statistics, short term statistics, long term statistics, mean, variance,autocorrelation function, cross correlation, moment generating function,time averaging, weighted averaging, special function, transcendentalfunction, Bessel function, error function, complementary error function.Beta function, Gamma function, integral function, Gaussian function,Poisson function, and another function.

Clause A17: The method/apparatus/system of the rhythmic motionmonitoring system of clause A8, further comprising: computing a numberof intermediate STT (ISTT) based on the STT of the N1*N2 TSCIC;monitoring the rhythmic motion of the object based on the number ofISTT.

Clause A18: The method/apparatus/system of the rhythmic motionmonitoring system of clause A17, further comprising: computing N1 ISTT,each ISTT based on N2 TSCIC; for each of the N1 TSCI, the ISTTassociated with the respective TSCI is a function of the STT of therespective N2 TSCIC in the overlapping segment, each of the respectiveN2 TSCIC being associated with one of the N2 CIC, wherein the functionto comprise at least one of: a pointwise function, a sliding function, alocalized function, a sliding localized function, a section-by-sectionfunction, a pitch estimation function, a hybrid function, scalarfunction, vector function, discrete function, continuous function,polynomial function, characteristics, feature, magnitude, phase,exponential function, logarithmic function, trigonometric function,transcendental function, logical function, linear function, algebraicfunction, nonlinear function, piecewise linear function, real function,complex function, vector-valued function, inverse function, derivativeof function, integration of function, circular function, function ofanother function, one-to-one function, one-to-many function, many-to-onefunction, many-to-many function, zero crossing, absolute function,indicator function, mean, mode, median, range, statistics, variance,arithmetic mean, geometric mean, harmonic mean, weighted mean, trimmedmean, conditional mean, percentile, maximum, minimum, square, cube,root, power, sine, cosine, tangent, cotangent, secant, cosecant,elliptical function, parabolic function, hyperbolic function, gamefunction, zeta function, absolute value, thresholding, limitingfunction, floor function, rounding function, sign function,quantization, piecewise constant function, composite function, functionof function, time function processed with an operation (e.g. filtering),probabilistic function, stochastic function, random function, ergodicfunction, stationary function, deterministic function, periodicfunction, transformation, frequency transform, inverse frequencytransform, discrete time transform, Laplace transform, Hilberttransform, sine transform, cosine transform, triangular transform,wavelet transform, integer transform, power-of-2 transform, sparsetransform, projection, decomposition, principle component analysis(PCA), independent component analysis (ICA), neural network, featureextraction, moving function, function of moving window of neighboringitems of time series, filtering function, convolution, mean function,variance function, statistical function, short-time transform, discretetransform, discrete Fourier transform, discrete cosine transform,discrete sine transform, Hadamard transform, eigen-decomposition,eigenvalue, singular value decomposition (SVD), singular value,orthogonal decomposition, matching pursuit, sparse transform, sparseapproximation, any decomposition, graph-based processing, graph-basedtransform, graph signal processing, classification, labeling, learning,machine learning, detection, estimation, feature extraction, learningnetwork, feature extraction, denoising, signal enhancement, coding,encryption, mapping, remapping, vector quantization, lowpass filtering,highpass filtering, bandpass filtering, matched filtering, Kalmanfiltering, preprocessing, postprocessing, particle filter, FIRfiltering, IIR filtering, autoregressive (AR) filtering, adaptivefiltering, first order derivative, high order derivative, integration,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, upsampling, interpolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, statistics, short term statistics, long term statistics, mean,variance, autocorrelation function, cross correlation, moment generatingfunction, time averaging, weighted averaging, special function,transcendental function, Bessel function, error function, complementaryerror function, Beta function, Gamma function, integral function,Gaussian function, Poisson function, and another function.

Clause A19: The method/apparatus/system of the rhythmic motionmonitoring system of clause A17, further comprising: computing N2 ISTT,each ISTT based on N1 TSCIC; for each of the N2 CIC, the ISTT associatedwith the respective CIC is a function of the STT of the respective N1TSCIC in the overlapping segment, each of the respective N1 TSCIC beingassociated with one of the N1 TSCI, wherein the function to comprise atleast one of: a pointwise function, a sliding function, a localizedfunction, a sliding localized function, a section-by-section function, apitch estimation function, a hybrid function, scalar function, vectorfunction, discrete function, continuous function, polynomial function,characteristics, feature, magnitude, phase, exponential function,logarithmic function, trigonometric function, transcendental function,logical function, linear function, algebraic function, nonlinearfunction, piecewise linear function, real function, complex function,vector-valued function, inverse function, derivative of function,integration of function, circular function, function of anotherfunction, one-to-one function, one-to-many function, many-to-onefunction, many-to-many function, zero crossing, absolute function,indicator function, mean, mode, median, range, statistics, variance,arithmetic mean, geometric mean, harmonic mean, weighted mean, trimmedmean, conditional mean, percentile, maximum, minimum, square, cube,root, power, sine, cosine, tangent, cotangent, secant, cosecant,elliptical function, parabolic function, hyperbolic function, gamefunction, zeta function, absolute value, thresholding, limitingfunction, floor function, rounding function, sign function,quantization, piecewise constant function, composite function, functionof function, time function processed with an operation (e.g. filtering),probabilistic function, stochastic function, random function, ergodicfunction, stationary function, deterministic function, periodicfunction, transformation, frequency transform, inverse frequencytransform, discrete time transform, Laplace transform, Hilberttransform, sine transform, cosine transform, triangular transform,wavelet transform, integer transform, power-of-2 transform, sparsetransform, projection, decomposition, principle component analysis(PCA), independent component analysis (ICA), neural network, featureextraction, moving function, function of moving window of neighboringitems of time series, filtering function, convolution, mean function,variance function, statistical function, short-time transform, discretetransform, discrete Fourier transform, discrete cosine transform,discrete sine transform, Hadamard transform, eigen-decomposition,eigenvalue, singular value decomposition (SVD), singular value,orthogonal decomposition, matching pursuit, sparse transform, sparseapproximation, any decomposition, graph-based processing, graph-basedtransform, graph signal processing, classification, labeling, learning,machine learning, detection, estimation, feature extraction, learningnetwork, feature extraction, denoising, signal enhancement, coding,encryption, mapping, remapping, vector quantization, lowpass filtering,highpass filtering, bandpass filtering, matched filtering, Kalmanfiltering, preprocessing, postprocessing, particle filter, FIRfiltering, IIR filtering, autoregressive (AR) filtering, adaptivefiltering, first order derivative, high order derivative, integration,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, upsampling, interpolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, statistics, short term statistics, long term statistics, mean,variance, autocorrelation function, cross correlation, moment generatingfunction, time averaging, weighted averaging, special function,transcendental function, Bessel function, error function, complementaryerror function, Beta function, Gamma function, integral function,Gaussian function, Poisson function, and another function.

Clause A20: The method/apparatus/system of the rhythmic motionmonitoring system of clause A17, further comprising: computing acombined STT (CSTT) based on the number of ISTT; monitoring the rhythmicmotion of the object based on the CSTT.

Clause A21: The method/apparatus/system of the rhythmic motionmonitoring system of clause A17, further comprising: computing acombined STT (CSTT) as a function of the number of ISTT: monitoring therhythmic motion of the object based on the CSTT, wherein the function tocomprise at least one of: scalar function, vector function, discretefunction, continuous function, polynomial function, characteristics,feature, magnitude, phase, exponential function, logarithmic function,trigonometric function, transcendental function, logical function,linear function, algebraic function, nonlinear function, piecewiselinear function, real function, complex function, vector-valuedfunction, inverse function, derivative of function, integration offunction, circular function, function of another function, one-to-onefunction, one-to-many function, many-to-one function, many-to-manyfunction, zero crossing, absolute function, indicator function, mean,mode, median, range, statistics, variance, arithmetic mean, geometricmean, harmonic mean, weighted mean, trimmed mean, conditional mean,percentile, maximum, minimum, square, cube, root, power, sine, cosine,tangent, cotangent, secant, cosecant, elliptical function, parabolicfunction, hyperbolic function, game function, zeta function, absolutevalue, thresholding, limiting function, floor function, roundingfunction, sign function, quantization, piecewise constant function,composite function, function of function, time function processed withan operation (e.g. filtering), probabilistic function, stochasticfunction, random function, ergodic function, stationary function,deterministic function, periodic function, transformation, frequencytransform, inverse frequency transform, discrete time transform, Laplacetransform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, sparse transform, projection, decomposition, principlecomponent analysis (PCA), independent component analysis (ICA), neuralnetwork, feature extraction, moving function, function of moving windowof neighboring items of time series, filtering function, convolution,mean function, variance function, statistical function, short-timetransform, discrete transform, discrete Fourier transform, discretecosine transform, discrete sine transform, Hadamard transform,eigen-decomposition, eigenvalue, singular value decomposition (SVD),singular value, orthogonal decomposition, matching pursuit, sparsetransform, sparse approximation, any decomposition, graph-basedprocessing, graph-based transform, graph signal processing,classification, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, smoothing, medianfiltering, mode filtering, sampling, random sampling, resamplingfunction, downsampling, upsampling, interpolation, extrapolation,importance sampling, Monte Carlo sampling, compressive sensing,statistics, short term statistics, long term statistics, mean, variance,autocorrelation function, cross correlation, moment generating function,time averaging, weighted averaging, special function, transcendentalfunction, Bessel function, error function, complementary error function.Beta function, Gamma function, integral function, Gaussian function,Poisson function, and another function.

Clause A22: The method/apparatus/system of the rhythmic motionmonitoring system of clause A17, further comprising: monitoring therhythmic motion of the object by computing at least one analytics basedon the number of ISTT.

Clause A23: The method/apparatus/system of the rhythmic motionmonitoring system of clause A20, further comprising: monitoring therhythmic motion of the object by computing at least one analytics basedon at least one of: the CSTT and the number of ISTT.

Clause A24: The method/apparatus/system of the rhythmic motionmonitoring system of one of clause A13 to clause A24 (i.e. one of the 10claims) further comprising: wherein each analytics is associated with atime stamp, a time window associated with the time stamp, the N1 TSCIrestricted to the time window and the N1*N2 TSCIC restricted to the timewindow; grouping a first analytics with a first time stamp and a secondanalytics with a second time stamp, wherein the second time stamp isadjacent to the first time stamp.

Clause A25: The method/apparatus/system of the rhythmic motionmonitoring system of clause A24 further comprising: grouping the firstanalytics and the second analytics based on a cost associated with thefirst analytics and the second analytics.

Clause A26: The method/apparatus/system of the rhythmic motionmonitoring system of clause A24 further comprising: grouping the firstanalytics and the second analytics based on a similarity score betweenthe first analytics and the second analytics.

Clause A27: The method/apparatus/system of the rhythmic motionmonitoring system of clause A24 further comprising: grouping the firstanalytics and the second analytics based on at least one of: asimilarity score between the first analytics and the second analytics,and a state transition cost between a first state associated with thefirst analytics and a second state associated with the second analytics.

Clause A28: The method/apparatus/system of the rhythmic motionmonitoring system of clause A24 further comprising: grouping a number ofanalytics with different time stamps into a time trace of analytics.

Clause A29: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28 further comprising: adding a number ofanalytics with different time stamps into a time trace of analytics byiteratively adding a later analytics to an existing time trace ofanalytics.

Clause A30: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28 further comprising: grouping the numberof analytics into the time trace of analytics based on a cost function.

Clause A31: The method/apparatus/system of the rhythmic motionmonitoring system of clause A30 further comprising: grouping the numberof analytics into the time trace of analytics based on a cost functionand an initial condition.

Clause A32: The method/apparatus/system of the rhythmic motionmonitoring system of clause A30 further comprising: grouping the numberof analytics into the time trace of analytics based on a cost functionand an ending condition.

Clause A33: The method/apparatus/system of the rhythmic motionmonitoring system of clause A30 further comprising: grouping the numberof analytics into the time trace of analytics based on a cost functionand a boundary condition of the analytics.

Clause A34: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28: wherein the cost function is based onat least one of: a similarity score between two analytics, a statetransition cost between a first state associated with a first analyticsand a second state associated with a second analytics, and a statetransition cost based on the first state associated with the firstanalytics and a state history associated with the first analytics.

Clause A35: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28: wherein the cost function favorsconsecutive analytics with large similarity and penalizes consecutiveanalytics with small similarity.

Clause A36: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28: wherein the cost function favorsconsecutive analytics with no state change and penalizes consecutiveanalytics with state change.

Clause A37: The method/apparatus/system of the rhythmic motionmonitoring system of clause A28 further comprising: computing at leastone time trace of analytics.

Clause A38: The method/apparatus/system of the rhythmic motionmonitoring system of clause A37 further comprising: computing a count ofthe object and other objects based on the at least one time trace ofanalytics.

Clause A39: The method/apparatus/system of the rhythmic motionmonitoring system of clause A38 further comprising: computing a count ofthe object and other objects based on a count of the at least one timetrace of analytics.

Clause A40: The method/apparatus/system of the rhythmic motionmonitoring system of clause A38 further comprising: computing a count ofthe object and other objects based on a count of the at least one timetrace of analytics that satisfies a condition.

Clause A41: The method/apparatus/system of the rhythmic motionmonitoring system of clause A38 further comprising: computing a countbased on a count of the at least one time trace of analytics.

Clause A42: The method/apparatus/system of the rhythmic motionmonitoring system of clause A1: wherein the response action to compriseat least one of: presenting at least one analytics associated with therhythmic motion of the object computed based on at least one of: theTSIC, and the TSCIC; presenting an analytics of the object; creating apresentation based on the rhythmic motion of the object; transmitting ananalytics of the rhythmic motion of the object to a user device;transmitting a message to the user device.

In various embodiments of the present teaching, monitoring and trackinga dynamic analytics trace may be performed according to the followingclauses.

Clause B1: A method/software/apparatus/system of an analytics tracefinding system, comprising: obtaining at least one time series ofchannel information (CI) of a wireless multipath channel of a venueusing a processor, a memory communicatively coupled with the processorand a set of instructions stored in the memory, wherein the at least onetime series of CI (TSCI) is extracted from a wireless signal transmittedbetween a Type 1 heterogeneous wireless device (wireless transmitter)and a Type 2 heterogeneous wireless device (wireless receiver) in thevenue through the wireless multipath channel; computing a first numberof analytics associated with a first time stamp based on the at leastone TSCI; computing a second number of analytics associated with asecond time stamp based on the at least one TSCI; grouping a firstanalytics with the first time stamp and a second analytics with thesecond time stamp; and triggering a response action based on thegrouping.

Clause B2: The method/software/apparatus/system of the analytics tracefinding system of clause B1, further comprising: grouping the firstanalytics and the second analytics based on a cost associated with thefirst analytics and the second analytics.

Clause B3: The method/software/apparatus/system of the analytics tracefinding system of clause B1, further comprising: grouping the firstanalytics and the second analytics based on a similarity scoreassociated with the first analytics and the second analytics.

Clause B4: The method/software/apparatus/system of the analytics tracefinding system of clause B1, further comprising: grouping the firstanalytics and the second analytics based on at least one of: asimilarity score between the first analytics and the second analytics,and a state transition cost between a first state associated with thefirst analytics and a second state associated with the second analytics.

Clause B5: A method/software/apparatus/system of an analytics tracefinding system, further comprising: obtaining at least one time seriesof channel information (CI) of a wireless multipath channel of a venueusing a processor, a memory communicatively coupled with the processorand a set of instructions stored in the memory, wherein the at least onetime series of CI (TSCI) is extracted from a wireless signal transmittedbetween a Type 1 heterogeneous wireless device (wireless transmitter)and a Type 2 heterogeneous wireless device (wireless receiver) in thevenue through the wireless multipath channel; for each of a series oftime stamps, computing a respective number of analytics for the timestamp based on the at least one TSCI; computing at least one analyticstrace based on the analytics, each analytics trace being a time seriesof analytics (TSA) within a respective time segment, wherein the TSA hasone analytics for each time stamp in the time segment.

Clause B6: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing eachanalytics trace based on a cost associated with the TSA.

Clause B7: The method/software/apparatus/system of the analytics tracefinding system of clause B6: wherein the cost to comprise at least oneof: a similarity score, and a state transition cost.

Clause B8: The method/software/apparatus/system of the analytics tracefinding system of clause B6: wherein the cost to comprise at least oneof: a similarity score between two analytics, a state transition costbetween a first state associated with a first analytics and a secondstate associated with a second analytics, a state transition cost basedon the first state associated with the first analytics and a statehistory associated with the first analytics, and a cumulative cost overthe respective time segment.

Clause B9: The method/software/apparatus/system of the analytics tracefinding system of clause B6: wherein the cost favors consecutiveanalytics with large similarity and penalizes consecutive analytics withsmall similarity.

Clause B10: The method/software/apparatus/system of the analytics tracefinding system of clause B6: wherein the cost favors consecutiveanalytics with no state change and penalizes consecutive analytics withstate change.

Clause B11: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing eachanalytics trace based on at least one of: an initial condition, anending condition, a boundary condition, and another condition.

Clause B12: The method/software/apparatus/system of the analytics tracefinding system of clause B5: computing each analytics trace based on atleast one of: an initial condition associated with an initial portion ofthe respective time segment, an ending condition associated with anending portion of the respective time segment, a condition associated aboundary of the respective time segment, a condition associated with atime of the respective time segment, and another condition.

Clause B13: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: adding a new analyticsto a current TSA, wherein time stamp associated with the new analyticsis later than all time stamps of the current TSA.

Clause B14: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: updating a current TSAbased on a new analytics, wherein time stamp associated with the newanalytics is later than all time stamps of the current TSA.

Clause B15: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: determining that ananalytics at a time stamp belongs to more than one analytics traces,being in more than one respective TSA.

Clause B16: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing the at leastone analytics trace based on at least one of: brute force searching,fast searching, genetic searching, divide-and-conquer searching, dynamicprogramming and another algorithm.

Clause B17: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecost for at least one candidate analytics trace for at least onecandidate time segment, computing the at least one analytics trace basedon all the candidate analytics trace and the associated cost.

Clause B18: The method/software/apparatus/system of the analytics tracefinding system of clause B17, further comprising: identifying acandidate analytics trace as one of the at least one analytics tracewhen the associated at least one cost satisfies at least one condition.

Clause B19: The method/software/apparatus/system of the analytics tracefinding system of clause B17: wherein the at least one condition tocomprise at least one of: an associated cost is smaller than an upperthreshold and larger than a lower threshold, each of the associated atleast one cost is smaller than a respective upper threshold and largerthan a respective lower threshold, an associated cost is local minimum,a combined cost is minimum, and another condition.

Clause B20: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount based on the at least one analytics trace.

Clause B21: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount based on a count of the at least one analytics trace.

Clause B22: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount based on a count of the at least one analytics trace thatsatisfied a condition.

Clause B23: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of people in the venue based on the at least one analytics trace.

Clause B24: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of people in the venue based on a count of the at least oneanalytics trace.

Clause B25: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of people in the venue based on a count of the at least oneanalytics trace that satisfied a condition.

Clause B26: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of objects in the venue based on the at least one analytics trace.

Clause B27: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of objects in the venue based on a count of the at least oneanalytics trace.

Clause B28: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount of objects in the venue based on a count of the at least oneanalytics trace that satisfied a condition.

Clause B29: The method/software/apparatus/system of the analytics tracefinding system of clause B5: wherein the analytics to comprise at leastone of: a frequency, a period, a periodicity parameter, a rhythm, anintensity, a phase, a musical note, a pitch, an expression with arhythm, a musical rhythm, a punctuated rhythm, a beat, a pace, a walkingrhythm, an exercise rhythm, a sports rhythm, a vibration parameter, anoscillation parameter, a pulsation parameter, a relaxing parameter, amotor parameter, an impulsive rhythm, an explosive rhythm, a dominantrhythm, a foreground rhythm, a background rhythm, a random rhythm, achaotic rhythm, a stochastic rhythm, a motion, a rhythmic motion, abreathing rate, a breathing period, a heart rate, a vital sign, a changeof the breathing rate, a change of the heart rate, a change of the vitalsign, a transient motion, a movement, an activity, a gait, a gesture, ahandwriting, a pattern, a trace, a history, a sign of danger, afall-down, a person, an information of the person, an object, aninformation of the object, a device, an identity, a count, a countassociated a rhythmic motion, a count of sources of rhythmic motion, acount of objects, a count of vehicles, a count of people, a count ofpets, a count of babies, a count of juveniles, a count of objects in acertain state, a count of stations in a certain state, a count of boothwithout occupancy, a change in the count, a scalar, a vector, a change,a rate of change, a derivative, an aggregate, an integration, a sum, aweighted mean, a trimmed mean, a moving average, a filtering, astatistics, a variance, a correlation, a covariance, a crosscorrelation, a cross covariance, an autocorrelation, a tag, a tagassociated with a motion, a tag associated with an object, an activityassociated with the rhythmic motion, a change in the tag, a state, anevent, an indicator of a state, an indicator of an event, SLEEP,NON-SLEEP, REM, NREM, AWAKE, MOTION, NO-MOTION, BATHROOM-VISIT, apresence/an absence, an emotional state, a movement parameter, alocation, a location associated with a map, a location associated with amotion, a location associated with an object, a change in the location,a distance, a displacement, a speed, a velocity, an acceleration, anangle, an angular speed, an angular acceleration, a trajectory, a time,a timing, a starting time/timing, a stopping time/timing, a pausingtime/timing, an interruption time/timing, arepeating/hourly/daily/weekly/monthly/yearly activity pattern, arepeating timing, movement pattern, a repeating trend, a daily activityassociated with the rhythmic motion, a daily activity, a spontaneousactivity, a variation, a variation of the frequency, a variation ofpitch, prosody, a variation of the period, a variation of rhythm, avariation of intensity, a variation of phase, a variation of parameter,a relationship between two rhythms, a cause-and-effect between tworhythms, and another rhythm parameter.

Clause B30: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: identifying at leastone of: an event, a movement, an action, an object based on the at leastone analytics trace.

Clause B31: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: triggering a responseaction based on the at least one analytics trace.

Clause B32: The method/software/apparatus/system of the analytics tracefinding system of clause B5, further comprising: computing at least onecount based on the at least one analytics trace; triggering a responseaction based on at least one of: the at least one analytics trace andthe at least one count.

In one example, the disclosed system may be used to obtain a quantity ofpeople in a venue with a rich-scattering environment, e.g. an amusementpark, a hospital, a room, a building, a bus, an airplane, a cruise ship,a train, etc. The disclosed system may find a trace of motion analyticsof people in the venue, based on channel information of a wirelessmultipath channel impacted by the people's motion. The trace may be atime trace/time trend, or a location trace/trajectory. For example, thedisclosed system may find a trace of breathing rate or heartbeat rate ofpeople in the venue, and determine the quantity of people in the venuebased on the trace.

In another example, the disclosed system may be used to recognize anidentity of a person at a location with a rich-scattering environment,e.g. a door, a gate, a monitored area in a building, etc. For each of aplurality of candidate persons in front of a door, the disclosed systemmay obtain a rate distribution of rhythmic motion (e.g. breathing,heartbeat) of the candidate person based on a training dataset. When avisitor is at the door, the disclosed system may compute a trace ofmotion analytics of the visitor, based on channel information of awireless multipath channel impacted by the visitor's rhythmic motion.For example, the disclosed system may find a trace of breathing rate orheartbeat rate of the visitor at the door, and determine a likelihoodfunction for the trace to be associated with each respective candidateperson in database, based on the rate distribution of rhythmic motion ofthe respective candidate persons. The disclosed system may recognize thevisitor to be a candidate person that maximizes the likelihood function.In one embodiment, if the maximized likelihood is still below athreshold, the disclosed system determines that the visitor is notrecognized.

In yet another example, the disclosed system may be used to recognize anidentity of a person in a car, e.g. whether the person is a particulardriver in the database. For example, after a driver enters the car, theentering action of the driver may be related to the driver's habit andmay be used for training a classifier and for driver recognition. Forexample, some drivers may enter the car and wait. Some drivers may resthands on steering wheel. Some drivers may wiggle body for a nicecomfortable position. Some drivers may put down a coffee. Some driversmay put down a bag. Some drivers may put a smart phone (with map apprunning) on dash board. Some drivers may press a button on dash board.Some drivers may check and adjust mirror. Some drivers may insert carkey. Some drivers may check and work on the phone, such as enterdestination on map. Similarly, the car-exiting action of the driver mayalso be related to the driver's habit and may be used for training aclassifier and for driver recognition. When a person enters or exits acar, the disclosed system can detect a motion and/or posture of theperson based on channel information of a wireless multipath channelimpacted by the people's motion and/or posture, and determine whetherthe person is a driver of the car based on whether the detected motionand/or posture matches a stored motion and/or posture of the driver inthe database.

The present teaching also discloses processing, compilation,organization, grouping and presentation of time-stamped data for avisual display or other feedback or user-interface (UI) device. Variousembodiments of the present teaching are regarding the processing,compilation, organization, grouping and presentation of time-stampeddata for a visual display (e.g. computer monitor, smart phone display,tablet, TV, projector, animation, etc.) or other feedback oruser-interface (UI) device (e.g. voice presentation via smart phones,computers, tablets, sound-enabled devices, a smart speaker such asAmazon Echo/Alexa, or vibration, or haptic device).

In one embodiment, each time-stamped data item may comprise a scalar, aduration, a timing, an ordered pair, an n-tuple, a coordinate, alocation, a direction, an angle, an attribute, a description, a trend, abehavior, a motion, a movement, a gesture (e.g. handwriting, hand sign,keystroke, facial expression), a vital sign, a feature, an object, anidentification, a characteristics, a phenomenon, an event (e.g.fall-down), a state, a transition, a status, a stage (e.g. sleep stage,awake, REM, NREM, etc.), a relationship, a vector, a matrix, aclassification, a detection, a decision, a conclusion, a set, a group, acollection, an element, a subset, and/or any mixture of these. The timestamped data may be associated with one or more time series of channelinformation (CI). The time-stamped data may comprise one or moreanalytics obtained/determined/computed based on the one or more timeseries of CI (e.g. channel state information, or CSI) extracted from awireless signal transmitted from a Type 1 heterogeneous wireless deviceto a Type 2 heterogeneous wireless device via a wireless multipathchannel of a venue.

In one embodiment, a Type 1 device may transmit the wireless signal tomultiple Type 2 devices. A Type 2 device may receive multiple wirelesssignals from multiple Type 1 devices, each wireless signal from acorresponding Type 1 device. A Type 1 device and a Type 2 device may bethe same device (i.e. operating like a radar system). A Type 1 deviceand a Type 2 device may be attached to/in/on/of a machine. The wirelesssignal may be a series of probe signals. The probe signals may be sentat regular, basically regular, or irregular time intervals. The probesignal may or may not be transmitted with data. The probe signal may bea data packet. Each probe signal when received may be time stamped.

In an example, one (or more) Type 1 device and one (or more) Type 2device may be placed around a bed (or anywhere) to monitor the motionand breathing of one (or more) person lying on the bed in the bedroom.For a bed with two people sleeping, a Type 1 device may be placed on oneend (e.g. the left) and another Type 1 device on another end (e.g. theright) to monitor the motion and breathing of two people simultaneously.The Type 2 device may be placed near the bed (e.g. at the front, at theback, underneath the mattress/bed, above the bed), or at anotherlocation in the house.

In another example, the Type 1 device(s) and Type 2 device(s) may beplaced at various places (e.g. at ceiling, on a wall, on the floor, on atable/furniture) of a house, an apartment, a warehouse, a parking lot, abuilding, a mall, a stadium, a station, an interchange, an office, aroom, a meeting room, a warehouse, a facility, a public facility, abathroom, a toilet, a staircase, a lift, etc. The Type 1 device(s)and/or Type 2 device(s) may be embedded in another device such as TV,remote control, set-top box, audio device, speaker, camera, router,access point, appliance, refrigerator, smoke detector, stove, furniture,chair, sofa, desk, table, vacuum cleaner, smoke detector, lock, tool,WiFi-enabled device, computer, printer, monitor, keyboard, mouse, mousepad, computer accessory, tablet, phone, clock, alarm clock, thermometer,thermostat, light device, light switch, power socket, power plug, lamp,bedside lamp, bed, computer, phone, tablet, smart plug, charger,extension device, power meter, toy, child item, baby item, baby monitor,adult item, elderly person item, health care item, IoT device, anotherhome device, an office device, a factory device, etc.

In one embodiment, there are N1 (e.g. N1=2 or 6 or 100 or 100000) timeseries of analytics (TSA), each item of each time series beingassociated with a time stamp. The analytic may be a motion intensityindex, a presence/absence, an approaching, a receding, a motionsequence, a motion indicator, a motion direction, a location, adistance, a speed, an acceleration, a timing, a duration, a time period,a periodic motion analytic, a frequency, a period, a transform, afunction/transformation of another analytic, a regularity, a transientmeasure, a manifestation, a revelation, a sign, a vital sign, an impact,a change, a deformation, a hand signal, a gesture signal, a healthsymptom, a duration, a count, a classification of motion, a breathingparameter/characteristics/statistics, a gait, a hand motion, gesture, abody language, a dancing move, a formation, an event, a state, status, astage, an indicator of a motion/an event/a condition/a situation/astate, a digital representation (e.g. taking on value of 0 and 1), acontinuous/analog representation (e.g. taking on any value between A andB, where A may be 0 or another value, B may be 1 or another value), etc.Some, if not all, analytics may be obtained/determined/computed based onthe channel information (CI).

Any TSA may be sampled/computed/obtained at regular or irregular timeinterval. In a TSA, the analytics may be obtained (sampled) regularlyfor some time, irregularly for some time, intermittently for some timeor spontaneously for some time. The analytics may be sampled at a lowrate (e.g. 10 Hz) in a time period (e.g. 1 hour), and at a higher rate(e.g. 1000 Hz) in another time period (e.g. 5 minutes). A temporalchange in the sampling may be in response to a change (e.g. inenvironment, detected motion/event/sign). For example, the sampling ratemay be low in a standby mode and may be changed to high in an alarmingmode or danger mode. For a particular time t, all or some or none of theN1 TSA may have an item associated with the time stamp (i.e. t).

There are two ways to present the N1 TSA. In the first way (same-graphpresentation), there is one graph with N1 curves (basically N1 graphssuperimposed on each other), with the y-axis (or y- and z-axes if ananalytics is 2-dimensional, or M-dimensional if an analytics has Mdimension) representing the N1 analytics and the x-axis representingtime. To maximize the visualization of each analytics, the range of they-axis may be mapped to different ranges of different analytics (e.g.from 0 to 1 for first analytics, from −10 to 10 for second analytics,from 0 to 100 for the third analytics, from 0 to 10 million for thefourth analytics and so on) while the x-axis is the time axis common toall the N1 curves. If a vertical line is drawn at a particular time t,it intersects with each curve at a point. Thus there are N1 intersectionpoints on the vertical line, one for each curve.

In the second way of presenting the N1 TSA (separate-graphpresentation), there are N1 separate graphs, one for each analytics. Forease of comparison, suppose the x-axes (time axes) of all graphs are thesame/identical, and suppose that all graphs are time synchronized, withessentially same “width” so that they can be conveniently stacked. Ineach graph, there is only one curve of the analytics (e.g. it may beidentical to the respective curve of the analytics drawn in thesame-graph presentation with same x-axis and y-axis). The N1 graphs arestacked/placed vertically (e.g. positioned one on top of another), withthe y-axis of the graphs co-linear (in a straight line) and the range ofthe y-axis of each graph mapped to the same respective range as in thesame-graph presentation. If a vertical line is drawn at time t in agraph, there is only one intersection in each graph—because there isonly one curve. If a vertical line is drawn at time t through the N1graphs, there will be N1 intersection points—one in each graph.

In one embodiment of the present teaching, the N1 TSA may be presentedin a novel hybrid way (hybrid presentation), with characteristics ofboth the same-graph and separate-graph presentation. In one way, the N1TSA may be combined to be a single combined TSA and the combined TSA maybe presented in a single combined graph with a single combined curve.Recall that in the separate-graph presentation, there may be N1 curvesin N1 graphs (1 curve for each graph) stacked/placed vertically. All thegraphs may have the same time axis or x-axis (synchronized). In thehybrid presentation, all the N1 graphs in the separate-grouppresentation may be merged into a “combined graph”. The common time axismay be partitioned to form M partitions (e.g. partition 1 from time t_0to time t_1, partition 2 from t_1 to t_2, . . . , partition M from timet_{M−1} to t_M). The M partitions may or may not span the whole timeaxis. In other words, there may or may not be gaps between adjacentpartitions. The length (or duration) of different partitions may bedifferent.

In the hybrid presentation, for each partition, only one of the N1curves may be selected (to form part of the combined curve) anddisplayed, while the rest of the N1 curves (the remaining N1−1 curves)may not be displayed. Alternatively, a new combined TSA may be formedwith the analytics in each partition chosen from the corresponding TSAassociated with the selected curve. The hybrid presentation isequivalent to displaying the new combined TSA.

One may define an indicator function I(t) which takes on integer valuesin the range of 1 to N1. For each partition, the indicator functionvalue is the value of the selected curve. For example, I(t)=k if thek{circumflex over ( )}{th} curve is the selected curve at time t. Onemay define I_k(t), with k=1, . . . , N1, to be an indicator function ofthe selection of the k{circumflex over ( )}{th} curve, taking on a valueof 1 if the k{circumflex over ( )}{th} curve is selected at time t, anda value of 0 otherwise. Then mathematically,

I(t)=sum_{k=1}{circumflex over ( )}{N1}k*I_k(t)

Let f_k(t) be the function corresponding to the k{circumflex over( )}{th} curve. Then the displayed function f(t) in the hybridpresentation may effectively be:

f(t)=sum_{k=1}{circumflex over ( )}{N1}f_k*I_k(t)

Alternatively, the indicator function, I(t), may itself be displayed inthe hybrid presentation to indicate which curve (TSA) is active,dominant, or selected, or highlighted, especially when the analytics areindicator of some mutually exclusive states (e.g. REM, NREM and AWAKEsleep states, or SLEEP and NO-SLEEP states, or MOTION and NO-MOTIONstates), or events (rest room visit).

Alternatively, all the N1 curves may be displayed in a first manner(e.g. in a non-dominant manner, in a subtle manner, in a backgroundmanner, and/or in less eye-catching manner, with light color, pastelcolor, grey color, unsaturated color, broken line, dotted line, lowerintensity, smaller line thickness, broken/dotted line type, lessintensity (e.g. zero intensity, i.e. not displayed), higher transparency(e.g. not visible, half-visible), and/or intermittent line type, etc.).For each partition, the selected curve may be displayed in a secondmanner (e.g. in a dominant manner, in a non-subtle manner, in aforeground manner, and/or in more eye-catching manner, with dark color,strong color, black, saturated color, solid line, higher intensity,larger line thickness, strong line type, more intensity, lowertransparency, and/or continuous line type, etc.) instead of the firstmanner. This is similar to displaying the N1 TSA as N1 curves in thefirst manner and the new combined TSA as a curve in the second manner.

In the alternative way of hybrid display, the N1 curves may still bedisplayed similar to how they would be displayed in the separate-graphpresentation. They may be stacked and placed vertically, similar to theseparate-graph presentation. Each may or may not be displayed with itsx-axis (or time axis), where the N1 x-axis may be replaced by a singleline (with/without markings to show time scale) to indicate the locationof axis.

Suppose the separation of adjacent graphs is P. Then, stacking the N1graphs is similar to forming and displaying the function

f(t)=sum_{k=1}{circumflex over ( )}{N1}(f_k+k*P)*I_k(t),

such that adjacent “curves” are separated by a distance of P. If onechooses not to display the f_k and set P=1, the displayed function willbe the indicator function I(t):

I(t)=sum_{k=1}{circumflex over ( )}{N1}k*I_k(t).

More generally, the function displayed may be

f(t)=sum_{k=1}{circumflex over ( )}{N1}(f_k+a_k)*I_k(t),

for some a_k values. In various embodiments, a_k can be k*P but can alsobe irregular.

Between the partitions, the curves may or may not be connected using aline (e.g. a straight line, a curve, etc.). If there is no gap betweentwo adjacent partitions, the curves in the two adjacent partitions maybe connected by a vertical line. In this way, there may be only onecombined curve in the combined graph, with the line segment in any timepartition being the line segment of one of the N1 curves. The combinedcurve may resemble a continuous function moving among the differentgraphs (or different curves). If a vertical line is drawn at time t (ofa particular partition), it intersects with the combined curve at onlyone point (on the selected curve associated with the partition). At timet, the selected graph may be considered the “current” graph. Theselected curve may be considered the “current” curve. Thepartition/segment may be considered the “current segment”. The segmentsand the axis may be labeled. In an example, N1=6 TSA in a well-beingmonitoring application with two Type 1 devices and one Type 2 device inthe home of a user living alone. A Type 1 device and the Type 2 devicemay be placed next to the user's bed. The other Type 1 device may beplaced in a rest room. There are two wireless links: one between thefirst Type 1 device and the Type 2 device, and one between the secondType 1 device and the Type 2 device. One (or more, if more than oneantenna/device) time series of channel information (TSCI) may beobtained for each wireless link based on a wireless signal beingtransmitted from the respective Type 1 device to the Type 2 device. Foreach wireless link, analytics (e.g. motion, breathing, etc.) may becomputed based on the respective TSCI.

In this example, there may be N1=6 analytics, comprising 6 indicatorsof: (I1) REM sleep, (I2) NREM sleep, (I3) awake, (I4) restroom, (I5)in-house activity, and (I6) no presence. In general, each indicator maytake on values of 1 and 0. An indicator value of 1 may mean thedescription is active/happening/assertive, while a value of 0 may meannot active/not happening/not assertive. When (I1) is 1, REM (rapid eyemovement) sleep may be detected (happening). When (I1) is zero, REMsleep may not be detected (not happening). The first three analytics,(I1) to (I3), may represent different stages of sleep (REM, NREM ornon-REM, AWAKE), active when the user is in SLEEP state (as opposed toNO-SLEEP state) typically taking turns to be asserted during bed time ofa user. In a way, (I1) to (I3) are three sub-states of the SLEEP state.That is, when the user has a good sleep, the AWAKE stage may be absent.These three analytics may be obtained from some sleep analysis procedurebased on the TSCI. In some situations, these three analytics may becombined as one combined sleep analytics taking on four values: REM,NREM, AWAKE and NOT ASLEEP. Typically, a function such as the combinedsleep analytics may be decomposed into, or represented as, a weightedsum of simple indicator functions. Analytics (I4) may be asserted whenactivity (e.g. motion) is detected in the rest room. Analytics (I5) maybe asserted when activity is detected in the rest of the house.Analytics (I6) may be asserted when no activity is detected. Typically,only one indicator is active while the others are not active.

In a separate-graph presentation, there may be 6 graphs, each with afunction (a curve). Let f_1(t) be the function for analytics (I1),f_2(t) for (I2), . . . , and f_6(t) for (I6). In the hybrid display, thecombined function may be displayed with the original 6 graphs stackedand labeled. For example the curve of (I1) may be labeled as “REM”; (I2)curve may be labeled as “NREM”, (I3) curve may be labeled as “Awake”,(I4) curve may be labeled as “Activities”, (I5) curve may be labeled as“Bathroom”, and (I6) curve may be labeled as “No activity”. Differentsections of the combined function may be labeled. For example, the usermay go to bed at 9 pm with the combined function taken on (I4)immediately before 9 pm and (I1) soon after 9 pm. The transition from(I4) to (I1) may be a vertical line labeled as “Go to bed”. Periods of(I5) may be labeled as “Bathroom”. A similar process may go on.

Each indicator may be associated with a value, e.g. (I1) associated witha_1, (I2) associated with a_2, (I3) associated with a_3, (I4) associatedwith a_4, (I5) associated with a_5, and (I6) associated with a_6. Thecombined function (or combined curve) may take on these values. Thecombined curve may be represented as(a_1+f_1(t))*I_1(t)+(a_2+f_2(t))*I_2(t)+(a_3+f_3(t))*I_3(t)+(a_4+f_4(t))*I_4(t)+(a_5+f_5(t))*I_5(t)+(a_6+f_6(t))*I_6(t).A possible combination is a_1=P, a_2=2P, a_3=3P, a_4=4P, a_5=5P, a_6=6Pfor some P so that the N1 graphs are spaced apart (e.g. withcorresponding x-axis at a distance of P apart). Another possiblecombination is a_1=a_2=a_3=a_4=a_5=a_6, in which case the hybridpresentation may degenerate into same-graph presentation.

In another embodiment, the analytics may be 2-dimensional and the TSAmay be presented as a curve in a 3-dimensional space (with x-axis beingtime, and y- and z-axes being the analytics) instead of a curve in2-dimensional space (with x-axis being time and y-axis being theanalytics). The N1 curves in 3-D space corresponding to the N1 TSA maybe stacked/placed in parallel. The time axis may be divided in the Mpartitions. In each partition, only one of the N1 curves may beselected/displayed, while the other (N1−1) curves are not displayed. Inyet another embodiment, the analytics may be K-dimensional and the TSAmay be presented as a curve in K-dimensional space. The N1 curvescorresponding to the N1 TSA may be spaced apart. The time axis may bedivided in the M partitions. In each partition, only one of the N1curves may be selected/displayed/animated/highlighted, while the other(N1−1) curves may not be selected/displayed/animated/highlighted.

The time-stamped data may be presented in a graphical user interface(GUI). The GUI may have a button which when clicked triggers a new pageto show the combined curve, e.g. combined curve of (I1), (I2), (I3),(I4), (I5) and (I6). The time scale (or time period) may be userselectable (e.g. 7-day, 24-hour period, 12-hour period, 8-hour period,1-hour, 30-minute, 15-minute, 5-minute, 1-minute). The user may click ona section of the combined curve corresponding to a particular timepartition with a particular selected curve. The click may cause a newpage to appear showing the selected curve. In the above example, whenthe user clicks on the combined graph where (I2) NREM sleep is selected,the new page may show the selected curve, (I2) NREM sleep.Alternatively, the new page may show the selected curve, (I2) NREMsleep, together with some related curves such as (I1) REM sleep, or (I3)AWAKE, because together (I1), (I2) and (I3) are sub-states of the SLEEPstate. In this case, the combined curve of (I1), (I2) and (I3) may bedisplay. Alternatively, (I1), (I2) and (I3) may be displayed using thetraditional same-graph presentation, or the traditional separate-graphpresentation. While the combined curve (e.g. of (I1), (I2), (I3), (I4),(I5) and/or (I6)) is shown, the user may select a subset of the curves,for example, (I1), (I2) and (I3). This may cause a new page to appearshowing a combined curve of (I1), (I2) and (I3). Alternatively, the newpage may show (I1), (I2) and (I3) using the same-graph presentation, orthe separate-graph presentation.

The GUI may show/display analytics such as (a) time for bed, (b) wake uptime, (c) total time of sleep, (d) number of awakening during mainsleep, (e) sleep score of main sleep, (f) number of times not at home orin room, (g) number of bathroom visits, (h) activity time duration, (i)no-activity time duration, (j) bathroom time duration. The analytics maybe computed for a user-selectable time scale (e.g. 8 hours, 12 hours, 24hours, 3 days, 7 days, 14 days, 1 month, 3 months, 6 months, 1 year).The instantaneous analytics may be displayed. An emoticon (or somegraphics showing good versus bad) may be displayed when the analytics isaverage, above average or below average. A history of the analytics maybe displayed for a time period (e.g. past week) at the user-selectabletime scale. For example, the number of bath room visits may be displayedfor a week, or a month, or a year to show long term trend and anyanomaly.

In another view of GUI, sleeping states may be displayed for a timewindow (e.g. 7 days). For each day, the sleeping may be represented by acolored bar. Period of NO-SLEEP, SLEEP, REM, NREM and AWAKE may berepresented by different colors. For example, NO-SLEEP may betransparent, AWAKE may have a light color, NREM may have a darker color,and REM may have a darkest color. The time window may be changed by theuser (e.g. previous 7 day, previous 7 days, next 7 days, next 7 days,etc.). The GUI may have a button which when clicked causes a new page toappear showing a monthly view, or a weekly view, or a daily view, or anhourly view, or a timed view.

FIG. 19 illustrates an exemplary day view showing separate instances ofsleep, according to some embodiments of the present teaching. A user cantoggle back and forth using the arrows to view the previous or nextdays' sleep (this also includes naps—there can be more than one sleep ina day). On the top, a hypnogram is displayed. These are the differentstages of sleep over the course of a night. On the bottom key sleepstatistics are displayed. Sleep Score is calculated using some proposedcustom equation that takes into account total sleep time and the time ineach stage. It is to give users a holistic view of how they slept.

FIG. 19A and FIG. 19B illustrate exemplary weekly views showing a 24hour scale, according to some embodiments of the present teaching. Sleepof each day(s) is displayed horizontally. The user can view sleep starttime, sleep end time, and the different stages throughout the night,represented with different shades of green (see legend). The user canzoom-in on chart to get a better view of these sleep stages. As shown,the X-axis changes to a smaller scale.

FIG. 20A and FIG. 20B illustrate exemplary home views showing real-timebreathing rate and movement index, according to some embodiments of thepresent teaching. Breathing rate is in breaths per minute, which willappear as a line that slides horizontally and is constantly changing.Movement index shows a rough picture of a sleeping person's motion. Thepoint is for it to show large movements (such as tossing and turning)that would cause the breathing rate to drop off. This can enable theuser to know that the sleeping person has not actually stoppedbreathing. There may be just motion that disrupts the breathing signal.Movement index will appear as vertical bars.

FIGS. 21-27 illustrate more exemplary views of lifelog display,according to some embodiments of the present teaching. In oneembodiment, in each of FIGS. 18-27, the elements on each display may beassociated with each other. For example, the hypnogram shown in FIG. 18is for sleep monitoring data within a day. But once the user clicks onthe “week” button at the bottom of the display in FIG. 18, the hypnogramwill automatically be changed to show sleep monitoring data within aweek. In one embodiment, after receiving an input from a user via a GUIdisplayed in any of FIGS. 18-27, the system performs data processingbased on the input and generates updated data for display via the GUI.For example, after the user clicks the “Services” button at the bottomof FIG. 27, and selects one or more services, the system may associatethe user's sleep data and/or other health or life related data to theseservices. As such, the system can present the user with these services(e.g. automatic 911 calling, reminder for sleeping, getting up, running,taking medicine, etc.) based on the user's sleep and/or life data logs.In addition, the system may collect, via the Type 1 and Type 2 devicesdescribed above, more life data of the user related to these services toimprove user experience of these services. The data collection can beperiodically based on a predetermined period, and/or dynamically inresponse to a user's input.

The following numbered clauses provide additional implementationexamples.

Clause E1: A method/system/software/device of the presentation system,comprising: determining more than one time series of analytics (TSA)based on a processor, a memory communicatively coupled with theprocessor and a set of instructions stored in the memory; determining acommon time axis for the more than one TSA; presenting the more than oneTSA synchronously and jointly in a hybrid manner based on the commontime axis.

Clause E2: The method/system/software/device of the presentation systemof Clause E1: wherein a first TSA is at least one of: dependent,independent, synchronous, and asynchronous, with respect to a secondTSA.

Clause E3: The method/system/software/device of the presentation systemof Clause E1: wherein sampling of a first TSA is at least one of:dependent, independent, synchronous, and asynchronous, with respect tosampling of a second TSA.

Clause E4: The method/system/software/device of the presentation systemof Clause E1: wherein a sampling attribute of a first TSA is at leastone of: the same as, similar to, and different from, the samplingattribute of a second TSA; wherein the attribute comprises at least oneof: time, frequency, period, interval, timing, time lag, time stamp,regularity, repetitiveness, variability, impulsiveness, pause, lapse,time-out, duration, source, type, sensor, memory, size, buffering,storage, mechanism, carrier frequency, carrier bandwidth, carrier band,modulation, precision, dynamic range, representation, fixed point,floating point, little endian, big endian, encapsulation, coding,encryption, scrambling, filtering, transformation, preprocessing,processing, postprocessing, noise floor, denoising, uncertainty,environment control, sensing condition, sensing setting, backgroundattribute, dependence, inter-dependence, co-dependence, triggering,priority, instantaneous behavior, short-term behavior, long-termbehavior, and another sensing attribute.

Clause E5: The method/system/software/device of the presentation systemof Clause E1, further comprising: wherein an analytics comprises atleast one of: scalar, vector, matrix, n-tuple, collection, set, subset,element, group, collection, mixture, Boolean, label, description,alphanumeric quantity, labeled quantity, time quantity, frequencyquantity, statistical quantity, event quantity, sliding quantity,time-stamped quantity, processed quantity, attribute, motion intensityindex, motion statistics, TRRS, presence/absence, approaching, receding,motion sequence, motion indicator, motion direction, securityinformation, safety information, intrusion, alarm, alert, location,localization, distance, speed, acceleration, angle, angular speed,angular acceleration, timing, duration, time period, periodic motionanalytic, frequency, period, transform, function/transformation ofanother analytic, distance measure of another two analytics, regularity,dependence, transient measure, manifestation, revelation, sign, vitalsign, breathing rate, heart rate, impact, change, deformation, handsignal, gesture signal, health symptom, duration, count, classificationof motion, health condition, biometric, sleep parameter, sleep score,sleep duration, sleep timing, sleep interruption, in-sleep, non-sleep,sleep stage, rapid-eye-movement (REM), non-REM (NREM), awake,detection/recognition/verification/tracking/monitoring/tracking/counting/locationing/localization/navigation/guidance/occurrence/co-occurrence/relationship/filtering/processing/preprocessing/postprocessing/correction/activation/accessing/parameter/characteristics/feature/representation/statistics/state/status/stage/condition/situation/indicator/transition/change/timing/classification/information of, ordeduction/inference/observation/summarization/decision/conclusion withrespect to, at least one of: intruder, people, user, pet, human, child,older adult, patient, intruder, pet, animal, object, material, tool,machine, device, car, defect, fault, motion, movement, motion sequence,event, presence, proximity, activity, daily activity, behavior,movement, phenomenon, history, trend, variation, change, regularity,irregularity, repetitiveness, periodic motion, repeated motion,stationary motion, cyclo-stationary motion, regular motion, breathing,heartbeat, vital sign, gait, motion/feature/cycle/characteristics ofbody parts/hand/elbow/arm/leg/foot/limbs/head/waist/wrist/eye duringwalking/running/exercise/locomotion/activity/man-machine interaction,transient motion, impulsive motion, sudden motion, fall-down, danger,life threat, user-interface, gesture, hand sign, handwriting, keystroke,facial expression, facial feature, emotion, body feature, body language,dancing movement, rhythmic movement, periodic motion, channelinformation (CI), channel state information (CSI), channel impulseresponse (CIR), channel frequency response (CFR), signal strength,angle-of-arrival (AoA), time-of-arrival (ToA), beamforming information,spectrum, derived analytics based on CI, and another analytics.

Clause E6: The method/system/software/device of the presentation systemof Clause E1, further comprising: computing at least one hybrid,synchronous, and joint (HSJ) presentation based on the more than one TSAand the common time axis, wherein HSJ presentation comprises at leastone of: a same-graph presentation, a separate-graph presentation and ahybrid presentation; presenting the at least one HSJ presentation on adevice.

Clause E7: The method/system/software/device of the presentation systemof Clause E6, further comprising: partitioning the common time axis intoa number of time segments, wherein the HSJ presentation is at least oneof: a graphical representation, a figure, and a plot, of the TSHA,wherein each time segment of the TSHA in the HSJ presentation isassociated with at least one of: a line, line type, line color, linewidth, line attribute, area, region, shading color, shading type,shading texture, shading attribute, boundary, boundary type, boundarycolor, boundary width, boundary attribute, surface, surface color,surface type, surface texture, surface shading, surface attribute,animation, flashing, fade-in, fade-out, transition effect, label,symbol, graphical effect, voice, volume, user-interface setting, music,sound effect, and another presentation attribute.

Clause E8: The method/system/software/device of the presentation systemof Clause E7, further comprising: wherein the number of time segmentsare consecutive and disjoint.

Clause E9: The method/system/software/device of the presentation systemof Clause E6, further comprising: computing a time series of hybridanalytics (TSHA) based on at least one of: the more than one TSA, andthe common time axis, wherein each hybrid analytics is associated with atime stamp; generating a HSJ presentation based on the TSHA.

Clause E10: The method/system/software/device of the presentation systemof Clause E9, further comprising: wherein the HSJ presentation is atleast one of: a graphical representation, an animation, a figure and aplot, of the TSHA.

Clause E11: The method/system/software/device of the presentation systemof Clause E10, further comprising: storing at least one of: the morethan one TSA, the TSHA, and the HSJ presentation, and the TSHA;communicating at least one of: the more than one TSA, the TSHA, and theHSJ presentation to the device. The TSHA may be indicator function ofrespective time segments (being constant in the respective time segmentsand taking on value (analytics ID) unique to the TSA.

Clause E12: The method/system/software/device of the presentation systemof Clause E9, further comprising: partitioning the common time axis intoa number of time segments; for each of the number of time segments:associating the time segment with one of the more than one TSA, andconstructing the time segment of the TSHA based on the association.

Clause E13: The method/system/software/device of the presentation systemof Clause E12: wherein the number of time segments are consecutive anddisjoint.

Clause E14: The method/system/software/device of the presentation systemof Clause E12, further comprising: associating each TSA with a uniqueanalytics ID, each analytics ID being a real number; for each of thenumber of time segments: assigning at least one hybrid analytics of theTSHA in the time segment to be the analytics ID associated with the timesegment.

Clause E15: The method/system/software/device of the presentation systemof Clause E14: wherein there are N1 TSA; wherein the unique analytics IDis one of N1 consecutive integers.

Clause E16: The method/system/software/device of the presentation systemof Clause E14: wherein there are N1 TSA; wherein the unique analytics IDis one of N1 equally spaced integers. The TSHA may be individual TSArestricted to respective time segments.

Clause E17: The method/system/software/device of the presentation systemof Clause E9, further comprising: partitioning the common time axis intoa number of time segments; for each of the number of time segments:associating each respective time segment with one of the more than oneTSA, and constructing the respective time segment of the TSHA based onthe respective time segment of the associated TSA.

Clause E18: The method/system/software/device of the presentation systemof Clause E17, further comprising: for each of the number of timesegments: constructing hybrid analytics in the respective time segmentof the TSHA by copying analytics from the respective time segment of theassociated TSA.

Clause E19: The method/system/software/device of the presentation systemof Clause E6, further comprising: computing more than one graphs usingthe common time axis, each associated with a TSA; synchronizing the morethan one graphs by restricting the graphs to a common time window and acommon time scale such that they have a similar width; stacking the morethan one synchronized graphs such that the time axes of the graphs areparallel and aligned; generating a HSJ by merging (or joining) the morethan one stacked synchronized graphs.

Clause E20: The method/system/software/device of the presentation systemof Clause E19, further comprising: scaling the TSA associated with eachgraph such that all stacked synchronized graphs have a similar height.

Clause E21: The method/system/software/device of the presentation systemof Clause E20, further comprising: partitioning the common axis into anumber of time segments; associating each time segment with one of themore than one TSA; constructing a highlighted graph by copying, for eachrespective time segment, respective stacked synchronized graphassociated with the respective time segment; generating the HSJ bypresenting the highlighted graph in a dominant way and the stackedsynchronized graphs in a subservient way.

Clause E22: The method/system/software/device of the presentation systemof Clause E21, further comprising: wherein each of: the highlightedgraph and the stacked synchronized graphs, in a time segment isassociated respectively with at least one of: a line, line type, linecolor, line width, line attribute, area, region, shading color, shadingtype, shading texture, shading attribute, boundary, boundary type,boundary color, boundary width, boundary attribute, surface, surfacecolor, surface type, surface texture, surface shading, surfaceattribute, animation, flashing, fade-in, fade-out, transition effect,label, symbol, graphical effect, voice, volume, user-interface setting,music, sound effect, and another presentation attribute.

Clause E23: The method/system/software/device of the presentation systemof Clause E22, further comprising: wherein a segment of the highlightedgraph is at least one of: connected, and not connected, with aneighboring segment of the highlighted graph; wherein the connection, ifany, is associated with a presentation attribute.

Clause E24: The method/system/software/device of the presentation systemof Clause E21, further comprising: stacking the more than onesynchronized graphs in a user-defined order.

Clause E25: The method/system/software/device of the presentation systemof Clause E21, further comprising: stacking a subset of the more thanone synchronized graphs such that the time axes of the graphs areparallel and aligned; generating another HSJ by merging (or joining) thesubset of more than one stacked synchronized graphs.

Clause E26: The method/system/software/device of the presentation systemof Clause E21, further comprising: generating another HSJ presentationbased on at least one of: another common time window, another timescale, another width, another scaling, another height, a filtering of aTSA, a processing of a TSA, a resampling of a TSA, another TSA, anothergraph.

Clause E27: The method/system/software/device of the presentation systemof Clause E6, further comprising: changing the HSJ presentation on thedevice to another HSJ presentation based on at least one of: akey-press, a user-selection, a device user-interface, a user command, avoice command, a user request, a plan, an animation sequence, a change,a warning, and a server command.

Clause E28: The method/system/software/device of the presentation systemof Clause E1, comprising: wherein a TSA is computed based on a timeseries of channel information (TSCI) of a wireless multipath channel;wherein the TSCI is extracted from a wireless signal transmitted from aType 1 heterogeneous wireless device to a Type 2 heterogeneous wirelessdevice through the wireless multipath channel in a venue.

Clause E29: The method/system/software/device of the presentation systemof Clause E28, comprising: wherein the wireless multipath channel isimpacted by a motion of an object in the venue; wherein the TSA isassociated with a monitoring task associated the motion of the object.

Clause E30: The method/system/software/device of the presentation systemof Clause E29, comprising: monitoring the motion of the object in thevenue; wherein the presentation of the more than one TSA synchronouslyand jointly in the hybrid manner is associated with the monitoring ofthe motion of the object in the venue.

Clause E31: The method/system/software/device of the presentation systemof Clause E30, comprising: wherein the monitoring task comprises atleast one of:detection/recognition/verification/tracking/monitoring/tracking/counting/locationing/localization/navigation/guidance/occurrence/co-occurrence/relationship/filtering/processing/preprocessing/postprocessing/correction/activation/accessing/parameter/characteristics/feature/representation/statistics/state/status/stage/condition/situation/indicator/transition/change/timing/classification/information of, ordeduction/inference/observation/summarization/decision/conclusion withrespect to, at least one of: intruder, people, user, pet, human, child,older adult, patient, intruder, pet, animal, object, material, tool,machine, device, car, defect, fault, motion, movement, motion sequence,event, presence, proximity, activity, daily activity, behavior,movement, phenomenon, history, trend, variation, change, regularity,irregularity, repetitiveness, periodic motion, repeated motion,stationary motion, cyclo-stationary motion, regular motion, breathing,heartbeat, vital sign, gait, motion/feature/cycle/characteristics ofbody parts/hand/elbow/arm/leg/foot/limbs/head/waist/wrist/eye duringwalking/running/exercise/locomotion/activity/man-machine interaction,transient motion, impulsive motion, sudden motion, fall-down, danger,life threat, user-interface, gesture, hand sign, handwriting, keystroke,facial expression, facial feature, emotion, body feature, body language,dancing movement, rhythmic movement, periodic motion, health,well-being, health condition, sleep, sleep stage, biometric, security,safety, intrusion, event, suspicious event, suspicious motion, alarm,alert, siren, location, distance, speed, acceleration, angle, angularspeed, angular acceleration, locationing, and map, energy management,power transfer, wireless power transfer, geometry estimation, maplearning, machine learning, machine learning, supervised learning,unsupervised learning, semi-supervised learning, clustering, featureextraction, featuring training, principal component analysis,eigen-decomposition, frequency decomposition, time decomposition,time-frequency decomposition, functional decomposition, otherdecomposition, training, discriminative training, supervised training,unsupervised training, semi-supervised training, neural network,augmented reality, wireless communication, data communication, signalbroadcasting, networking, coordination, administration, encryption,protection, cloud computing, and another monitoring task.

The features described above may be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., C, Java), including compiled orinterpreted languages, and it may be deployed in any form, including asa stand-alone program or as a module, component, subroutine, abrowser-based web application, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, e.g., both general and special purpose microprocessors, digitalsignal processors, and the sole processor or one of multiple processorsor cores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

While the present teaching contains many specific implementationdetails, these should not be construed as limitations on the scope ofthe present teaching or of what may be claimed, but rather asdescriptions of features specific to particular embodiments of thepresent teaching. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Anycombination of the features and architectures described above isintended to be within the scope of the following claims. Otherembodiments are also within the scope of the following claims. In somecases, the actions recited in the claims may be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

The features described above may be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., C, Java), including compiled orinterpreted languages, and it may be deployed in any form, including asa stand-alone program or as a module, component, subroutine, abrowser-based web application, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, e.g., both general and special purpose microprocessors, digitalsignal processors, and the sole processor or one of multiple processorsor cores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

While the present teaching contains many specific implementationdetails, these should not be construed as limitations on the scope ofthe present teaching or of what may be claimed, but rather asdescriptions of features specific to particular embodiments of thepresent teaching. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Anycombination of the features and architectures described above isintended to be within the scope of the following claims. Otherembodiments are also within the scope of the following claims. In somecases, the actions recited in the claims may be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

We claim:
 1. A system for rhythmic motion monitoring, comprising: atransmitter configured for transmitting a wireless signal through awireless multipath channel that is impacted by a rhythmic motion of anobject in a venue; a receiver configured for: receiving the wirelesssignal through the wireless multipath channel, and extracting N1 timeseries of channel information (TSCI) of the wireless multipath channelfrom the wireless signal modulated by the wireless multipath channelthat is impacted by the rhythmic motion of the object, wherein each ofthe N1 TSCI is associated with an antenna of the transmitter and anantenna of the receiver; and a processor configured for: decomposingeach of the N1 TSCI into N2 time series of channel informationcomponents (TSCIC), wherein a channel information component (CIC) ofeach of the N2 TSCIC at a time comprises a respective component of achannel information (CI) of the TSCI at the time, monitoring therhythmic motion of the object based on at least one of: the N1*N2 TSCICand the N1 TSCI, wherein N1 and N2 are positive integers, and triggeringa response action based on the monitoring of the rhythmic motion of theobject.
 2. The system of claim 1, wherein: each CI of the N1 TSCI isassociated with a time stamp; each CI of the N1 TSCI comprises at leastone of: N2 frequency-domain components, and N2 time-domain components;and the processor is further configured for performing at least one ofthe following: transforming, by a frequency transform, each CI with N2time-domain components to another CI with N2 frequency-domaincomponents, transforming, by an inverse frequency transform, each CIwith N2 frequency-domain components to another CI with N2 time-domaincomponents, and uniformly re-sampling each CI with time stamps evenlyspaced in time.
 3. The system of claim 1, wherein the processor isfurther configured for: segmenting each TSCI into overlapping segmentsof CI, wherein each of the overlapping segments comprises CI eachassociated with a time stamp in a sliding time window; and monitoringthe rhythmic motion of the object in each of the overlapping segments.4. The system of claim 1, wherein the processor is further configuredfor: segmenting each TSCIC into overlapping segments of CIC, whereineach of the overlapping segments comprises CIC each with a time stamp ina sliding time window; and monitoring the rhythmic motion of the objectbased on the N1*N2 TSCIC in each of the overlapping segments.
 5. Thesystem of claim 4, wherein the processor is further configured for:adjusting the sampling time of at least one CI of a TSCI such that allCI of the TSCI and all CIC of the N2 associated TSCIC are uniformlysampled with time stamps evenly spaced in time; computing a short-timetransformation (STT) of each of the N1*N2 TSCIC in each of theoverlapping segments after adjusting the sampling time; and monitoringthe rhythmic motion of the object based on STTs of the N1*N2 TSCIC ineach of the overlapping segments.
 6. The system of claim 5, wherein: thesampling time of a CI is adjusted by interpolating a re-sampled CI at adesirable time stamp based on the CI and its neighboring CI; and thesampling time of a CIC associated with the CI is adjusted by at leastone of: copying the respective component of the re-sampled CI, andinterpolating a respective re-sampled CIC at the desirable time stampbased on the CIC and its neighboring CIC.
 7. The system of claim 5,wherein monitoring the rhythmic motion of the object comprises:computing N1*N2 analytics associated with the rhythmic motion of theobject based on the STTs of the N1*N2 TSCIC; and computing a combinedanalytics based on a function of the N1*N2 analytics, wherein each ofthe N1*N2 analytics is associated with a respective one of the N1*N2TSCIC.
 8. The system of claim 5, wherein monitoring the rhythmic motionof the object comprises: computing a number of intermediate STTs (ISTTs)based on the STTs of the N1*N2 TSCIC in each of the overlappingsegments; and monitoring the rhythmic motion of the object based on theISTTs.
 9. The system of claim 8, wherein monitoring the rhythmic motionof the object comprises: computing N1 ISTTs, wherein each of the N1ISTTs is associated with one of the N1 TSCI and is computed based onrespective N2 TSCIC associated with the TSCI in each of the overlappingsegments.
 10. The system of claim 9, wherein each of the N1 ISTTs iscomputed as a function of the STTs of the N2 TSCIC in the overlappingsegment.
 11. The system of claim 8, wherein monitoring the rhythmicmotion of the object comprises: computing N2 ISTTs, where each of the N2ISTTs is associated with one of the N2 components of any SI and iscomputed based on respective N1 TSCIC associated with the component ineach of the overlapping segments.
 12. The system of claim 11, whereineach of the N2 ISTTs is computed as a function of the STTs of therespective N1 TSCIC in the overlapping segment.
 13. The system of claim8, wherein monitoring the rhythmic motion of the object comprises:computing a combined STT (CSTT) based on a function of the number ofISTTs; and monitoring the rhythmic motion of the object based at leastpartially on the CSTT.
 14. The system of claim 1, wherein monitoring therhythmic motion of the object comprises computing at least one analyticsassociated with the rhythmic motion of the object based on a processingof at least one of: the N1 TSCI and the N1*N2 TSCIC, wherein the atleast one analytics comprises at least one of: a frequency, a period, aperiodicity parameter, a rhythm, an intensity, a phase, a musical note,a pitch, an expression with a rhythm, a musical rhythm, a punctuatedrhythm, a beat, a pace, a walking rhythm, an exercise rhythm, a sportsrhythm, a vibration parameter, an oscillation parameter, a pulsationparameter, a relaxing parameter, a motor parameter, an impulsive rhythm,an explosive rhythm, a dominant rhythm, a foreground rhythm, abackground rhythm, a random rhythm, a chaotic rhythm, a stochasticrhythm, a breathing rate, a breathing period, a heart rate, a vitalsign, a change of the breathing rate, a change of the heart rate, achange of the vital sign, a quantity, a quantity associated the rhythmicmotion of the object, a quantity of sources of rhythmic motion, aquantity of objects in the venue, a quantity of people, a change in thequantity, a tag, a tag associated with the rhythmic motion, a tagassociated with the object, an activity associated with the rhythmicmotion, a change in the tag, a location, a location associated with therhythmic motion, a location associated with the object, a change in thelocation, a time, a timing, a starting time, a starting timing, astopping time, a stopping timing, a pausing time, a pausing timing, aninterruption time, an interruption timing, a repeating pattern, anhourly pattern, a daily pattern, a weekly pattern, a monthly pattern, ayearly activity pattern, a repeating timing, movement pattern, arepeating trend, a daily activity associated with the rhythmic motion, adaily activity, a spontaneous activity, a variation, a variation of thefrequency, a variation of pitch, prosody, a variation of the period, avariation of rhythm, a variation of intensity, a variation of phase, avariation of parameter, a relationship between two rhythms, acause-and-effect between two rhythms, and another rhythm parameter. 15.The system of claim 14, wherein: each of the at least one analytics isassociated with: a time stamp, a time window associated with the timestamp, the N1 TSCI within the time window and the N1*N2 TSCIC within thetime window; monitoring the rhythmic motion of the object comprises:grouping a first analytics associated with a first time stamp and asecond analytics associated with a second time stamp that is adjacent tothe first time stamp.
 16. The system of claim 15, wherein the firstanalytics and the second analytics are grouped based on at least one of:a cost associated with the first analytics and the second analytics; asimilarity score between the first analytics and the second analytics;and a state transition cost between a first state associated with thefirst analytics and a second state associated with the second analytics.17. The system of claim 14, wherein monitoring the rhythmic motion ofthe object comprises: grouping a number of analytics with different timestamps into a time trace of analytics; and iteratively adding a lateranalytics to the time trace of analytics.
 18. The system of claim 17,wherein the number of analytics with different time stamps are groupedinto the time trace of analytics based on a cost function and at leastone of: an initial condition of the analytics; an ending condition ofthe analytics; and a boundary condition of the analytics.
 19. The systemof claim 18, wherein: the cost function is based on at least one of: asimilarity score between two analytics, a state transition cost betweena first state associated with a first analytics and a second stateassociated with a second analytics, and a state history associated withthe first analytics.
 20. The system of claim 18, wherein: the costfunction favors consecutive analytics with a larger similarity andpenalizes consecutive analytics with a smaller similarity; and the costfunction favors consecutive analytics with no state change and penalizesconsecutive analytics with a state change.
 21. The system of claim 14,wherein monitoring the rhythmic motion of the object comprises:computing at least one time trace of analytics based on the at least oneanalytics; and computing a quantity of objects including the object inthe venue based on at least one of: the at least one time trace ofanalytics, and a quantity of the at least one time trace of analyticsthat satisfies a condition.
 22. The system of claim 14, wherein theprocessor is further configured for: for each of a plurality ofcandidate objects, obtaining a rate distribution of rhythmic motion ofthe candidate object based on a training dataset; computing at least onetime trace of analytics based on the at least one analytics; computing alikelihood function for the at least one time trace of analytics to beassociated with each respective candidate object, based on the ratedistribution of rhythmic motion of the respective candidate object; andrecognizing, based on hypothesis-testing, the object to be a candidateobject that maximizes the likelihood function.
 23. The system of claim1, wherein the response action comprises at least one of: presenting atleast one analytics associated with the rhythmic motion of the objectcomputed based on at least one of: the N1 TSCI, and the N1*N2 TSCIC;presenting an analytics of the object; creating a presentation based onthe rhythmic motion of the object; transmitting an analytics of therhythmic motion of the object to a user device; and transmitting amessage to the user device.
 24. A method, implemented by a processor, amemory communicatively coupled with the processor, and a set ofinstructions stored in the memory to be executed by the processor,comprising: obtaining N1 time series of channel information (TSCI) of awireless multipath channel that is impacted by a rhythmic motion of anobject in a venue, wherein the N1 TSCI is extracted from a wirelesssignal transmitted from a transmitter to a receiver through the wirelessmultipath channel, wherein each of the N1 TSCI is associated with anantenna of the transmitter and an antenna of the receiver; decomposingeach of the N1 TSCI into N2 time series of channel informationcomponents (TSCIC), wherein a channel information component (CIC) ofeach of the N2 TSCIC at a time comprises a respective component of achannel information (CI) of the TSCI at the time, wherein N1 and N2 arepositive integers; monitoring the rhythmic motion of the object based onat least one of: the N1*N2 TSCIC and the N1 TSCI; and triggering aresponse action based on the monitoring of the rhythmic motion of theobject.
 25. The method of claim 24, wherein the response actioncomprises at least one of: presenting at least one analytics associatedwith the rhythmic motion of the object computed based on at least oneof: the N1 TSCI, and the N1*N2 TSCIC; presenting an analytics of theobject; creating a presentation based on the rhythmic motion of theobject; transmitting an analytics of the rhythmic motion of the objectto a user device; and transmitting a message to the user device.
 26. Themethod of claim 24, wherein: each CI of the N1 TSCI is associated with atime stamp; and each CI of the N1 TSCI comprises: (a) N2frequency-domain components, (b) N2 time-domain components, or (c) N2frequency-domain components and N2 time-domain components.
 27. Themethod of claim 26, further comprising at least one of the following:transforming, by a frequency transform, each CI with N2 time-domaincomponents to another CI with N2 frequency-domain components;transforming, by an inverse frequency transform, each CI with N2frequency-domain components to another CI with N2 time-domaincomponents; and uniformly re-sampling each CI with time stamps evenlyspaced in time.
 28. The method of claim 24, further comprising:segmenting each TSCI into overlapping segments of CI, wherein each ofthe overlapping segments comprises CI each associated with a time stampin a sliding time window; and monitoring the rhythmic motion of theobject in each of the overlapping segments.
 29. The method of claim 24,further comprising: segmenting each TSCIC into overlapping segments ofCIC, wherein each of the overlapping segments comprises CIC each with atime stamp in a sliding time window; and monitoring the rhythmic motionof the object based on the N1*N2 TSCIC in each of the overlappingsegments.
 30. An apparatus for rhythmic motion monitoring in a venuewhere a transmitter and a receiver are located, comprising: at least oneof the transmitter and the receiver, wherein: the transmitter isconfigured for transmitting a wireless signal through a wirelessmultipath channel that is impacted by a rhythmic motion of an object inthe venue, the receiver is configured for receiving the wireless signalthrough the wireless multipath channel, and extracting N1 time series ofchannel information (TSCI) of the wireless multipath channel from thewireless signal modulated by the wireless multipath channel that isimpacted by the rhythmic motion of the object, each of the N1 TSCI isassociated with an antenna of the transmitter and an antenna of thereceiver; and a processor configured for: decomposing each of the N1TSCI into N2 time series of channel information components (TSCIC),wherein a channel information component (CIC) of each of the N2 TSCIC ata time comprises a respective component of a channel information (CI) ofthe TSCI at the time, monitoring the rhythmic motion of the object basedon at least one of: the N1*N2 TSCIC and the N1 TSCI, wherein N1 and N2are positive integers, and triggering a response action based on themonitoring of the rhythmic motion of the object.